Data Science and Management最新文献

{"title":"Design of knowledge transaction protection mechanism in the open innovation community based on blockchain technology","authors":"Dongshan Yang ,&nbsp;Ling Zhao ,&nbsp;Feng Leng ,&nbsp;Zujun Shi","doi":"10.1016/j.dsm.2024.08.002","DOIUrl":"10.1016/j.dsm.2024.08.002","url":null,"abstract":"<div><div>Open innovation communities (OICs) are important for the development of modern enterprises. However, there are still many safety hazards and trust crises on community platforms. This paper proposes an approach that combines blockchain technology and an OIC, as blockchain technology provides a direction for solving trust and security problems in community platform transactions. Moreover, the design of smart contracts can ensure the implementation of transactions and improve their reliability. In this study, the participants in an OIC and three types of infringement problems were analyzed. The Byzantine Fault Tolerant (BFT) algorithm was applied to design the logic of smart contracts and the coupling between idea platforms and the blockchain. The protection mechanism of an OIC was established based on blockchain smart contract supervision to provide a better knowledge co-creation environment for OICs.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 1","pages":"Pages 86-93"},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143296563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of feature selection and normalization on network intrusion detection","authors":"Mubarak Albarka Umar ,&nbsp;Zhanfang Chen ,&nbsp;Khaled Shuaib ,&nbsp;Yan Liu","doi":"10.1016/j.dsm.2024.08.001","DOIUrl":"10.1016/j.dsm.2024.08.001","url":null,"abstract":"<div><div>The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence (AI) techniques (such as machine learning (ML) and deep learning (DL)) to build more efficient and reliable intrusion detection systems (IDSs). However, the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs. Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues. While most of these researchers reported the success of these preprocessing techniques on a shallow level, very few studies have been performed on their effects on a wider scale. Furthermore, the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used, which most of the existing studies give little emphasis on. Thus, this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets: NSL-KDD, UNSW-NB15, and CSE–CIC–IDS2018, and various AI algorithms. A wrapper-based approach, which tends to give superior performance, and min-max normalization methods were used for feature selection and normalization, respectively. Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization. The models were evaluated using popular evaluation metrics in IDS modeling, intra- and inter-model comparisons were performed between models and with state-of-the-art works. Random forest (RF) models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86% and 96.01%, respectively, whereas artificial neural network (ANN) achieved the best accuracy of 95.43% on the CSE–CIC–IDS2018 dataset. The RF models also achieved an excellent performance compared to recent works. The results show that normalization and feature selection positively affect IDS modeling. Furthermore, while feature selection benefits simpler algorithms (such as RF), normalization is more useful for complex algorithms like ANNs and DNNs, and algorithms such as NB are unsuitable for IDS modeling. The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset. Our findings suggest that prioritizing robust algorithms like RF, alongside complex models such as ANN and DNN, can significantly enhance IDS performance. These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 1","pages":"Pages 23-39"},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143163848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital volunteer services in emergency situations: Typological characteristics, advantages, and challenges","authors":"Yongzhong Sha ,&nbsp;Xingfei Wei ,&nbsp;Chunhua Niu ,&nbsp;Yongbao Zhang ,&nbsp;Lu He","doi":"10.1016/j.dsm.2024.08.003","DOIUrl":"10.1016/j.dsm.2024.08.003","url":null,"abstract":"<div><div>Volunteer engagement in emergency management, focusing on mitigating adverse consequences, has attracted scholarly and practitioner attention. Digital volunteering helps overcome the limitations of traditional on-site volunteering through extensive volunteering opportunities in emergency management. This study utilizes a case text analysis and interviews to investigate and categorize digital volunteer services in emergency scenarios. Based on two key dimensions—direct recipients of volunteer services and the nature of the services rendered—the study presents four types of digital volunteer services: bridging, supportive, complementary, and collaborative. Moreover, it delineates eight role archetypes digital volunteers assume in emergency response situations along with their primary service contributions. Compared to conventional on-site volunteer services, digital volunteer services offer unique advantages while facing specific challenges. Finally, this study offers recommendations in four dimensions for the robust development of digital volunteer services, contributing to more effective and sustainable emergency management practices.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 1","pages":"Pages 1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecast uncertainties real-time data-driven compensation scheme for optimal storage control","authors":"Arbel Yaniv,&nbsp;Yuval Beck","doi":"10.1016/j.dsm.2024.07.002","DOIUrl":"10.1016/j.dsm.2024.07.002","url":null,"abstract":"<div><div>This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts, which are integral to an optimal energy storage control system. By expanding on an existing algorithm, this study resolves issues discovered during implementation and addresses previously overlooked concerns, resulting in significant enhancements in both performance and reliability. The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model, which is utilized for illustrative simulations to highlight its potential efficacy on a real site. Furthermore, a comprehensive comparison with the original formulation was conducted to cover all possible scenarios. This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system. Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management, significant financial losses and penalties, and potential contract violations. The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements. These advancements yield a reliable and efficient real-time correction algorithm for optimal site management, designed as an independent white box that can be integrated with any day-ahead optimization control system.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 1","pages":"Pages 59-71"},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141846117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-market quantitative trading: The dynamics of liquidity and turnover in financial markets","authors":"Qing Zhu ,&nbsp;Chenyu Han ,&nbsp;Yuze Li","doi":"10.1016/j.dsm.2024.07.003","DOIUrl":"10.1016/j.dsm.2024.07.003","url":null,"abstract":"<div><div>Financial market liquidity is a popular research topic. Investor-driven research uses the turnover rate to measure liquidity and generally finds that the higher the stock turnover rate, the lower the returns. However, the traditional financial liquidity theory has been impacted by new machine-driven quantitative trading models. To explore high machine-driven liquidity and the impact of high turnover rates on returns, this study establishes a dual-market quantitative trading system, introduces a variational modal decomposition (VMD)-bidirectional gated recurrent unit (BiGRU) model for data prediction, and uses the back-end Hong Kong foreign exchange market to develop a quantitative trading strategy using the same rotating funds in the U.S. and Chinese stock markets. The experimental results show that given a principal amount of 210,000.00 CNY, the final predicted net return is 226,538.30 CNY, a net return of 107.86%, which is 40.6% higher than the net return of a single Chinese market. We conclude that, under machine-driven trading, increasing liquidity and turnover increase returns. This study provides a new perspective on liquidity theory that is useful for future financial market research and quantitative trading practices.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141851279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A model for predicting dropout of higher education students","authors":"Anaíle Mendes Rabelo,&nbsp;Luis Enrique Zárate","doi":"10.1016/j.dsm.2024.07.001","DOIUrl":"10.1016/j.dsm.2024.07.001","url":null,"abstract":"<div><div>Higher education institutions are becoming increasingly concerned with the retention of their students. This work is motivated by the interest in predicting and reducing student dropout, and consequently in reducing the financial losses of said institutions. Based on the characterization of the dropout problem and the application of a knowledge discovery process, an ensemble model is proposed to improve dropout prediction. The ensemble model combines the results of three models: Logistic Regression, Neural Networks, and Decision Tree. As a result, the model can correctly classify 89% of the students as enrolled or dropped and accurately identify 98.1% of dropouts. When compared with the Random Forest ensemble method, the proposed model demonstrates desirable characteristics to assist management in proposing actions to retain students.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 1","pages":"Pages 72-85"},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141853113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative study of IoT- and AI-based computing disease detection approaches","authors":"Wasiur Rhmann ,&nbsp;Jalaluddin Khan ,&nbsp;Ghufran Ahmad Khan ,&nbsp;Zubair Ashraf ,&nbsp;Babita Pandey ,&nbsp;Mohammad Ahmar Khan ,&nbsp;Ashraf Ali ,&nbsp;Amaan Ishrat ,&nbsp;Abdulrahman Abdullah Alghamdi ,&nbsp;Bilal Ahamad ,&nbsp;Mohammad Khaja Shaik","doi":"10.1016/j.dsm.2024.07.004","DOIUrl":"10.1016/j.dsm.2024.07.004","url":null,"abstract":"<div><div>The emergence of different computing methods such as cloud-, fog-, and edge-based Internet of Things (IoT) systems has provided the opportunity to develop intelligent systems for disease detection. Compared to other machine learning models, deep learning models have gained more attention from the research community, as they have shown better results with a large volume of data compared to shallow learning. However, no comprehensive survey has been conducted on integrated IoT- and computing-based systems that deploy deep learning for disease detection. This study evaluated different machine learning and deep learning algorithms and their hybrid and optimized algorithms for IoT-based disease detection, using the most recent papers on IoT-based disease detection systems that include computing approaches, such as cloud, edge, and fog. Their analysis focused on an IoT deep learning architecture suitable for disease detection. It also recognizes the different factors that require the attention of researchers to develop better IoT disease detection systems. This study can be helpful to researchers interested in developing better IoT-based disease detection and prediction systems based on deep learning using hybrid algorithms.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 1","pages":"Pages 94-106"},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141841404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Creating non-fungible token (NFT)-backed emoji art from user conversations on blockchain","authors":"Maedeh Mosharraf,&nbsp;Mohammad Hossein Khorrami","doi":"10.1016/j.dsm.2024.06.002","DOIUrl":"10.1016/j.dsm.2024.06.002","url":null,"abstract":"<div><div>In the metaverse, digital assets are essential to define identity, shape the virtual environment, and facilitate economic transactions. This study introduces a novel feature to the metaverse by capturing a fundamental aspect of individuals–their conversations–and transforming them into digital assets. It utilizes natural language processing and machine learning methods to extract key sentences from user conversations and match them with emojis that reflect their sentiments. The selected sentence, which encapsulates the essence of the user’s statements, is then transformed into digital art through a generative visual model. This digital artwork is transformed into a non-fungible token, becoming a valuable digital asset within the blockchain ecosystem that is ideal for integration into metaverse applications. Our aim is to manage personality traits as digital assets to foster individual uniqueness, enrich user experiences, and facilitate more personalized services and interactions with both like-minded users and non-player characters, thereby enhancing the overall user journey.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 1","pages":"Pages 40-47"},"PeriodicalIF":0.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143164911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Value realization of intelligent emergency management: research framework from technology enabling to value creation","authors":"Yan Guo ,&nbsp;Yan Song ,&nbsp;Mingyue Zhang","doi":"10.1016/j.dsm.2024.06.001","DOIUrl":"10.1016/j.dsm.2024.06.001","url":null,"abstract":"<div><div>This study investigated the application and the application value of intelligent emergency in emergency management in the big data environment. It addresses the neglect of the application value (performance) measurement of intelligent emergency, further improving the effectiveness of intelligent emergency management. First, approximately 3900 documents from the intelligent emergency field are analyzed to determine the future research trend in intelligent emergency management. The socio-technical theory concerning technical and social systems is introduced. The emergency management system concepts of “technology enabling” and “enabling value creation” are defined according to bibliometric analysis and socio-technical theory. Second, a research framework that includes technology enabling and enabling value creation for the decision-making paradigm in emergency management according to the big data environment is constructed. A detailed analysis approach from intelligent emergency technology enabling to enabling value creation in emergency management is proposed. Finally, earthquake disasters are taken as examples, and specific analyses of the intelligent emergency enabling and enabling value creation are explored; enabling value creation is discussed based on measurable indicators. The clear concept of emergency management system technology enabling and enabling value creation, as well as the detailed analysis approach from intelligent emergency technology enabling to enabling value creation, provide a theoretical bases for scholars and practitioners to evaluate the value (performance) of intelligent emergency for the first time.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 1","pages":"Pages 11-22"},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141409093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing cyber threat detection with an improved artificial neural network model","authors":"Toluwase Sunday Oyinloye ,&nbsp;Micheal Olaolu Arowolo ,&nbsp;Rajesh Prasad","doi":"10.1016/j.dsm.2024.05.002","DOIUrl":"10.1016/j.dsm.2024.05.002","url":null,"abstract":"<div><div>Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems (IDS). Data labeling difficulties, incorrect conclusions, and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for cybersecurity. To overcome these obstacles, researchers have created several network IDS models, such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning techniques. This study provides an updated learning strategy for artificial neural network (ANN) to address data categorization problems caused by unbalanced data. Compared to traditional approaches, the augmented ANN’s 92% accuracy is a significant improvement owing to the network’s increased resilience to disturbances and computational complexity, brought about by the addition of a random weight and standard scaler. Considering the ever-evolving nature of cybersecurity threats, this study introduces a revolutionary intrusion detection method.</div></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":"8 1","pages":"Pages 107-115"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141276095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}