Advancement of management information system for discovering fraud in master card based intelligent supervised machine learning and deep learning during SARS-CoV2

During coronavirus (SARS-CoV2) the number of fraudulent transactions is expanding at a rate of alarming (7,352,421 online transaction records). Additionally, the Master Card (MC) usage is increasing. To avoid massive losses, companies of finance must constantly improve their management information systems for discovering fraud in MC. In this paper, an approach of advancement management information system for discovering of MC fraud was developed using sequential modeling of data depend on intelligent forecasting methods such as deep Learning and intelligent supervised machine learning (ISML). The Long Short-Term Memory Network (LSTM), Logistic Regression (LR), and Random Forest (RF) were used. The dataset is separated into two parts: the training and testing data, with a ratio of 8:2. Also, the advancement of management information system has been evaluated using 10-fold cross validation depend on recall, f1-score, precision, Mean Absolute Error (MAE), Receiver Operating Curve (ROC), and Root Mean Square Error (RMSE). Finally various techniques of resampling used to forecast if a transaction of MC is genuine/fraudulent. Performance for without re-sampling, with under-sampling, and with over-sampling is measured for each Algorithm. Highest performance of without re-sampling was 0.829 for RF algorithm-F score. While for under-sampling, it was 0.871 for LSTM algorithm-RMSE. Further, for over-sampling, it was 0.921 for both RF algorithm-Precision and LSTM algorithm-F score. The results from running advancement of management information system revealed that using resampling technique with deep learning LSTM generated the best results than intelligent supervised machine learning.     

A quantitative stock prediction system based on financial news

We examine the problem of discrete stock price prediction using a synthesis of linguistic, financial and statistical techniques to create the Arizona Financial Text System (AZFinText).The research within this paper seeks to contribute to the AZFinText system by comparing AZFinText’s predictions against existing quantitative funds and human stock pricing experts. We approach this line of research using textual representation and statistical machine learning methods on financial news articles partitioned by similar industry and sector groupings. Through our research, we discovered that stocks partitioned by Sectors were most predictable in measures of Closeness, Mean Squared Error (MSE) score of 0.1954, predicted Directional Accuracy of 71.18% and a Simulated Trading return of 8.50% (compared to 5.62% for the S&P 500 index). In direct comparisons to existing market experts and quantitative mutual funds, our system’s trading return of 8.50% outperformed well-known trading experts. Our system also performed well against the top 10 quantitative mutual funds of 2005, where our system would have placed fifth. When comparing AZFinText against only those quantitative funds that monitor the same securities, AZFinText had a 2% higher return than the best performing quant fund.     

Design and Implementation of an Efficient Electronic Bank Management Information System Based Data Warehouse and Data Mining Processing

The quantity of electronic bank data grows exponentially with development of Information Technology (IT). The size of these data is impossible for traditional database and human analyst to come up with interesting information that will help in process of decision making. Management Information System (MIS) based Data warehouse (DW) and Data Mining (DM) techniques support the development of IT and process of management decision-making. But the traditional DW size make the query complex, which may cause unacceptable delay in decision support queries. Thus, in this paper an Efficient Electronic Bank MIS based DW and Mining Processing (EEBMIS-DWMP) was developed with cluster and non-cluster indexed view to provide decision-makers with both best response time and precise information. Also, analysis of the multilayer perception neural network, naïve Bayes, random forest, logistic regression, support vector machine and C5.0 on a real-world data of bank was done to improve effectiveness for campaign by analyzing the most useful features that influence campaign success. Results offer how the proposed EEBMIS-DWMP developed bank organizations by comparing performance of system with and without index view in terms of balance accuracy, accuracy, precision, recall, mean absolute error, root mean square error, F measure and running time. Conclusions from results offers that EEBMIS-DWMP can construct a database for each customer, a storage system that integrates data from a variety of sources into a single unified framework, decrease errors and time required to prepare financial reports, quickly access for information, analysis of data in multivariate, accurate prediction of competent, profitability segmentation.     

Machine learning-enabled multiplexed microfluidic sensors

High-throughput, cost-effective, and portable devices can enhance the performance of point-of-care tests. Such devices are able to acquire images from samples at a high rate in combination with microfluidic chips in point-of-care applications. However, interpreting and analyzing the large amount of acquired data is not only a labor-intensive and time-consuming process, but also prone to the bias of the user and low accuracy. Integrating machine learning (ML) with the image acquisition capability of smartphones as well as increasing computing power could address the need for high-throughput, accurate, and automatized detection, data processing, and quantification of results. Here, ML-supported diagnostic technologies are presented. These technologies include quantification of colorimetric tests, classification of biological samples (cells and sperms), soft sensors, assay type detection, and recognition of the fluid properties. Challenges regarding the implementation of ML methods, including the required number of data points, image acquisition prerequisites, and execution of data-limited experiments are also discussed.     

基于用户的管理信息系统的研究方法

管理信息系统（简称MIS）主要用于开发和利用企业的信息资源,其研究方法是管理信息系统学科领域中的一个重要问题,基于用户的管理信息系统的研究方法主要有系统科学方法、数学方法、定性研究方法等。     

An intelligent blockchain-based system for safe vaccine supply and supervision

Immunization is an indispensable mechanism for preventing infectious diseases in modern society, and vaccine safety is closely related to public health and national security. However, issues such as vaccine expiration and vaccine record fraud are still widespread in vaccine supply chains. Therefore, an effective management system for the supervision of vaccine supply chains is urgently required. As the next generation of core technology after the Internet, blockchain is designed to build trust mechanisms that can change current information management methods. Meanwhile, the development of machine learning technologies provides additional ways to analyze the data in information management systems. The main objective of this study is to develop a “vaccine blockchain” system based on blockchain and machine learning technologies. This vaccine blockchain system is designed to support vaccine traceability and smart contract functions, and can be used to address the problems of vaccine expiration and vaccine record fraud. Additionally, the use of machine learning models can provide valuable recommendations to immunization practitioners and recipients, allowing them to choose better immunization methods and vaccines.     

Beyond effective use: Integrating wise reasoning in machine learning development

The introduction of machine learning (ML), as the engine of many artificial intelligence (AI)-enabled systems in organizations, comes with the claim that ML models provide automated decisions or help domain experts improve their decision-making. Such a claim gives rise to the need to keep domain experts in the loop. Hence, data scientists, as those who develop ML models and infuse them with human intelligence during ML development, interact with various ML stakeholders and reflect their views within ML models. This interaction comes with (often conflicting) demands from various ML stakeholders and potential tensions. Building on the theories of effective use and wise reasoning, this mixed method study proposes a model to better understand how data scientists can use wisdom for managing these tensions when they develop ML models. In Study 1, through interviewing 41 analytics and ML experts, we investigate the dimensions of wise reasoning in the context of ML development. In Study 2, we test the overall model using a sample of 249 data scientists. Our results confirm that to develop effective ML models, data scientists need to not only use ML systems effectively, but also practice wise reasoning in their interactions with domain experts. We discuss the implications of these findings for research and practice.     

浅析地勘单位内部会计控制

企业内部会计控制是指企业为了确保财产的安全、完整,防止贪污舞弊现象,保证会计资料和信息的真实性、准确性、客观性以及财务活动的合法性和有效性而采取的控制措施。     

Knowledge-sparse and knowledge-rich learning in information retrieval

This paper reviews some aspects of the relationship between the large and growing fields of machine learning (ML) and information retrieval (IR). Learning programs are described along several dimensions. One dimension refers to the degree of dependence of an ML + IR program on users, thesauri, or documents. This paper emphasizes the role of the thesaurus in ML + IR work. ML + IR programs are also classified in a dimension that extends from knowledge-sparse learning at one end to knowledge-rich learning at the other. Knowledge-sparse learning depends largely on user yes-no feedback or on word frequencies across documents to guide adjustments in the IR system. Knowledge-rich learning depends on more complex sources of feedback, such as the structure within a document or thesaurus, to direct changes in the knowledge bases on which an intelligent IR system depends. New advances in computer hardware make the knowledge-sparse learning programs that depend on word occurrences in documents more practical. Advances in artificial intelligence bode well for knowledge-rich learning.     

A machine learning based framework to identify unseen classes in open-world text classification

Classical supervised machine learning (ML) follows the assumptions of closed-world learning. However, this assumption does not work in an open-world dynamic environment. Therefore, the automated systems must be able to discover and identify unseen instances. Open-world ML can deal with unseen instances and classes through a two-step process: (1) discover and classify unseen instances and (2) identify novel classes discovered in step (1). Most existing research on open-world machine learning (OWML) only focuses on step 1. However, performing step 2 is required to build intelligent systems. The proposed framework comprises three different but interconnected modules that discover and identify unseen classes. Our in-depth performance evaluation establishes that the proposed framework improves open accuracy by up to 8% compared to the state-of-the-art models.     

Semi-supervised document retrieval

This paper proposes a new machine learning method for constructing ranking models in document retrieval. The method, which is referred to as SSRank, aims to use the advantages of both the traditional Information Retrieval (IR) methods and the supervised learning methods for IR proposed recently. The advantages include the use of limited amount of labeled data and rich model representation. To do so, the method adopts a semi-supervised learning framework in ranking model construction. Specifically, given a small number of labeled documents with respect to some queries, the method effectively labels the unlabeled documents for the queries. It then uses all the labeled data to train a machine learning model (in our case, Neural Network). In the data labeling, the method also makes use of a traditional IR model (in our case, BM25). A stopping criterion based on machine learning theory is given for the data labeling process. Experimental results on three benchmark datasets and one web search dataset indicate that SSRank consistently and almost always significantly outperforms the baseline methods (unsupervised and supervised learning methods), given the same amount of labeled data. This is because SSRank can effectively leverage the use of unlabeled data in learning.     

面向知识发现的药物ADMET情报预测方法

【目的/意义】挖掘药物筛选工作中的隐性知识,借助机器学习的预测能力替代生物实验方法,减少制药流程的研发时间和经济成本。【方法/过程】提出一种面向知识发现的ADMET情报预测理论框架,以4种传统机器学习方法和2种集成学习方法,分别构建6种分类预测模型,提取药物的隐性知识,比较不同模型的优越性,评估最优模型的经济价值。【结果/结论】以药物分子描述符信息预测ADMET具有可行性,6种模型性能表现综合排序结果为随机森林、梯度提升决策树、Logistic回归、支持向量机、K近邻、高斯朴素贝叶斯。前沿信息技术能够有效应用于药物知识发现,信息经济学分析可预见创造可观收益,是未来制药工艺降本增效的重要手段。【创新/局限】未来应融合专家知识、追加试验验证、丰富参考指标。     

Developing scalable management information system with big financial data using data mart and mining architecture

The traditional Management Information System (MIS) with Big Financial Data (BFD) for corporate financial diagnosis has many limitations such as the data is not summarized thus these causing increases in query times, and also the complexity in analysis. The creation of a Data Mart (DM) leads to a great summarization of data, such that contains only essential business information. And by using data mining techniques we can be extracting unknown useful information from DM and apply it to make important decisions for the business. Thus, in this paper we are adopting an architecture of six layers; interface layer, analysis layer, extract transformation load layer, data mart layer, data mining layer, and evaluating layer, MIS with BFD using DM and Mining (MIS-BFD-DMM) is proposed, which is not only permits the use of DM and mining technologies in decision support, but also the full utilization of non-financial/financial info held by businesses. This paper offers the benefits of building and integrating DM with mining. Also determines the distinction between DM and a relational database for decision-makers to get information. The test and analysis are achieved in the terms of useful metrics (accuracy, balance accuracy, F-measure, precision, recall, and time). As a result, Data returned from arranged star schema is far faster than ERD. In conclusion, the SVM is best than other algorithms in terms of the parameters of the confusion matrix.     

Optimized hadoop map reduce system for strong analytics of cloud big product data on amazon web service

Because of the rapid increase of data in the cloud of Amazon Web Service (AWS), the traditional methods for analyzing this data are not good and inappropriate, so unconventional methods of analysis have been proposed by many data scientists such as concurrent/ parallel techniques to meeting the requirements of performance and scalability entailed in such big data analyses. In this paper we are used Hadoop Map Reduce system that contains Hadoop Distributed File System (HDFS) and Hadoop cluster. We optimized it by combining it with five efficient Data Mining (DM) algorithms such as Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Correlative Naïve Bayes classifier (CNB), and Fuzzy CNB (FCNB) for strong analytics of cloud big data. The proposed system applied on product review data that taken form the cloud of AWS. The Evaluation of Hadoop Map Reduce done with important benchmarks as Mean Absolute Percentage Error (MPAE), Root Mean Square Error (RMSE), and runtime for word count, sort, inverted index. Also, the evaluation of DM models with Hadoop Map Reduce system done by using accuracy, sensitivity, specificity, memory, and running time. Experiments have shown that FCNB is effective in addressing the problem of big data.     

Papers with code or without code? Impact of GitHub repository usability on the diffusion of machine learning research

Open Science initiatives prompt machine learning (ML) researchers and experts to share source codes - "scientific artifacts" - alongside research papers via public repositories such as GitHub. Here we analyze the extent to which 1) the availability of GitHub repositories influences paper citation and 2) the popularity trend of ML frameworks (e.g., PyTorch and TensorFlow) affects article citation rates. To accomplish this, we connect ML research publications indexed by Papers with Code (PwC) to Microsoft Academic Graph (MAG) and collect repository-level metadata using the GitHub API. Applying nearest-neighbor matching and econometric considerations, we estimate that papers enjoy approximately 20% advantages in monthly citation rates after the creation of the first GitHub repositories, accounting for paper-level fixed effects and ages. We also find that the temporal popularity trends for frameworks used in the first associated repositories could influence the monthly citation rate for papers. The results highlight the importance of technological artifacts and infrastructure latent to the diffusion of research.     

科学基金资助F0701的科学计量分析

2018年,科学基金信息科学领域增设“教育信息科学与技术”申请代码(F0701)资助教育科学基础研究。基于首批F0701科学基金申请与资助项目数据的科学计量分析显示：申请项目涵盖了个性化教学、教育大数据、机器学习、增强现实、教育机器人、学习评测、交互学习、数字资源、协同学习、资源配置等十个主题聚类。研究发现,目前的教育信息科学与技术研究仍处于技术迁移期,主要以信息领域向教育领域渗透的研究工作为主,但对教育领域的重大关键科学问题缺乏深刻凝练,深度交叉融合不足。建议研究者加强对自然科学研究范式的运用、增强研究团队的交叉融合、提高凝练科学问题的能力;建议科学基金进一步充实完善申请代码,引导评审专家根据本领域项目申请的特点进行评估,提高资助率并加大支持力度,促进我国教育信息科学与技术领域整体研究水平的提升。     

Exploring human resource management intelligence practices using machine learning models

The use of machine learning for recruitment has become one of the main themes in human resources ever since machine learning software investigated the first recruitment software and discovered that utilizing technology improves their effectiveness at work, speed, and makes the process simpler. In order to better handle employee files, profiles, turnover, data analytics, and the creation of electronic personal data sheets for government service records, a human resource information system that incorporates machine learning has been created. Using a supervised machine learning technique, it was designed to foresee staff turnover. From a theoretical perspective, machine learning apps may be able to perform the same tasks as HR specialists, if not better or faster. Supporting HR professionals in becoming a true business partner and providing them with accurate and reliable advice, the interaction between HR professionals and line top management believes that the HR professionals still has surplus over machine learning, alone. Human resources methods and the significance of machine learning are the primary focus of this paper. This paper's three goals are to (1) determine how much of an impact Machine learning can have on the organization's recruitment procedures, (2) examine the extent to which this technology has been adopted, and (3) examine the frequency with which complaints have been lodged during these crucial exercises.     

A local and global event sentiment based efficient stock exchange forecasting using deep learning

Stock exchange forecasting is an important aspect of business investment plans. The customers prefer to invest in stocks rather than traditional investments due to high profitability. The high profit is often linked with high risk due to the nonlinear nature of data and complex economic rules. The stock markets are often volatile and change abruptly due to the economic conditions, political situation and major events for the country. Therefore, to investigate the effect of some major events more specifically global and local events for different top stock companies (country-wise) remains an open research area. In this study, we consider four countries- US, Hong Kong, Turkey, and Pakistan from developed, emerging and underdeveloped economies’ list. We have explored the effect of different major events occurred during 2012–2016 on stock markets. We use the Twitter dataset to calculate the sentiment analysis for each of these events. The dataset consists of 11.42 million tweets that were used to determine the event sentiment. We have used linear regression, support vector regression and deep learning for stock exchange forecasting. The performance of the system is evaluated using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results show that performance improves by using the sentiment for these events.