The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,s...The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.展开更多
With the rapid development of data science and artificial intelligence technology,its application in education in the field of extensive,which is of great significance to promote educational equity.By collecting and a...With the rapid development of data science and artificial intelligence technology,its application in education in the field of extensive,which is of great significance to promote educational equity.By collecting and analyzing students’data,personalized learning provides customized learning path;the intelligent auxiliary education system provides personalized guidance to reduce the burden of teachers.This paper discusses the strategies of data science and artificial intelligence in promoting educational equity,including the establishment of a comprehensive student data collection and analysis system and the promotion of intelligent auxiliary education system,aiming to realize the optimal allocation of educational resources,so that every student can enjoy fair and high-quality education.展开更多
Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and futur...Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and future jobs, and thus student careers. At the heart of this digital transformation is data science, the discipline that makes sense of big data. With many rapidly emerging digital challenges ahead of us, this article discusses perspectives on iSchools＇ opportunities and suggestions in data science education. We argue that iSchools should empower their students with ＂information computing＂ disciplines, which we define as the ability to solve problems and create values, information, and knowledge using tools in application domains. As specific approaches to enforcing information computing disciplines in data science education, we suggest the three foci of user-based, tool-based, and application- based. These three loci will serve to differentiate the data science education of iSchools from that of computer science or business schools. We present a layered Data Science Education Framework （DSEF） with building blocks that include the three pillars of data science （people, technology, and data）, computational thinking, data-driven paradigms, and data science lifecycles. Data science courses built on the top of this framework should thus be executed with user-based, tool-based, and application-based approaches. This framework will help our students think about data science problems from the big picture perspective and foster appropriate problem-solving skills in conjunction with broad perspectives of data science lifecycles. We hope the DSEF discussed in this article will help fellow iSchools in their design of new data science curricula.展开更多
Purpose： The purpose of the paper is to provide a framework for addressing the disconnect between metadata and data science. Data science cannot progress without metadata research.This paper takes steps toward advanc...Purpose： The purpose of the paper is to provide a framework for addressing the disconnect between metadata and data science. Data science cannot progress without metadata research.This paper takes steps toward advancing the synergy between metadata and data science, and identifies pathways for developing a more cohesive metadata research agenda in data science. Design/methodology/approach： This paper identifies factors that challenge metadata research in the digital ecosystem, defines metadata and data science, and presents the concepts big metadata, smart metadata, and metadata capital as part of a metadata lingua franca connecting to data science. Findings： The ＂utilitarian nature＂ and ＂historical and traditional views＂ of metadata are identified as two intersecting factors that have inhibited metadata research. Big metadata, smart metadata, and metadata capital are presented as part ofa metadata linguafranca to help frame research in the data science research space. Research limitations： There are additional, intersecting factors to consider that likely inhibit metadata research, and other significant metadata concepts to explore. Practical implications： The immediate contribution of this work is that it may elicit response, critique, revision, or, more significantly, motivate research. The work presented can encourage more researchers to consider the significance of metadata as a research worthy topic within data science and the larger digital ecosystem. Originality/value： Although metadata research has not kept pace with other data science topics, there is little attention directed to this problem. This is surprising, given that metadata is essential for data science endeavors. This examination synthesizes original and prior scholarship to provide new grounding for metadata research in data science.展开更多
Since its launch in 2011, the Materials Genome Initiative（MGI） has drawn the attention of researchers from academia,government, and industry worldwide. As one of the three tools of the MGI, the use of materials data...Since its launch in 2011, the Materials Genome Initiative（MGI） has drawn the attention of researchers from academia,government, and industry worldwide. As one of the three tools of the MGI, the use of materials data, for the first time, has emerged as an extremely significant approach in materials discovery. Data science has been applied in different disciplines as an interdisciplinary field to extract knowledge from data. The concept of materials data science has been utilized to demonstrate its application in materials science. To explore its potential as an active research branch in the big data era, a three-tier system has been put forward to define the infrastructure for the classification, curation and knowledge extraction of materials data.展开更多
In present digital era,data science techniques exploit artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to have an impact and develop their businesses.Data science integr...In present digital era,data science techniques exploit artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to have an impact and develop their businesses.Data science integrates the conventions of econometrics with the technological elements of data science.It make use of machine learning(ML),predictive and prescriptive analytics to effectively understand financial data and solve related problems.Smart technologies for SMEs enable allows the firm to get smarter with their processes and offers efficient operations.At the same time,it is needed to develop an effective tool which can assist small to medium sized enterprises to forecast business failure as well as financial crisis.AI becomes a familiar tool for several businesses due to the fact that it concentrates on the design of intelligent decision making tools to solve particular real time problems.With this motivation,this paper presents a new AI based optimal functional link neural network(FLNN)based financial crisis prediction(FCP)model forSMEs.The proposed model involves preprocessing,feature selection,classification,and parameter tuning.At the initial stage,the financial data of the enterprises are collected and are preprocessed to enhance the quality of the data.Besides,a novel chaotic grasshopper optimization algorithm(CGOA)based feature selection technique is applied for the optimal selection of features.Moreover,functional link neural network(FLNN)model is employed for the classification of the feature reduced data.Finally,the efficiency of theFLNNmodel can be improvised by the use of cat swarm optimizer(CSO)algorithm.A detailed experimental validation process takes place on Polish dataset to ensure the performance of the presented model.The experimental studies demonstrated that the CGOA-FLNN-CSO model has accomplished maximum prediction accuracy of 98.830%,92.100%,and 95.220%on the applied Polish dataset Year I-III respectively.展开更多
There has long been discussion about the distinctions of library science,information science,and informatics,and how these areas differ and overlap with computer science.Today the term data science is emerging that ge...There has long been discussion about the distinctions of library science,information science,and informatics,and how these areas differ and overlap with computer science.Today the term data science is emerging that generates excitement and questions about how it relates to and differs from these other areas of study.展开更多
The rise or fall of the stock markets directly affects investors’interest and loyalty.Therefore,it is necessary to measure the performance of stocks in the market in advance to prevent our assets from suffering signi...The rise or fall of the stock markets directly affects investors’interest and loyalty.Therefore,it is necessary to measure the performance of stocks in the market in advance to prevent our assets from suffering significant losses.In our proposed study,six supervised machine learning(ML)strategies and deep learning(DL)models with long short-term memory(LSTM)of data science was deployed for thorough analysis and measurement of the performance of the technology stocks.Under discussion are Apple Inc.(AAPL),Microsoft Corporation(MSFT),Broadcom Inc.,Taiwan Semiconductor Manufacturing Company Limited(TSM),NVIDIA Corporation(NVDA),and Avigilon Corporation(AVGO).The datasets were taken from the Yahoo Finance API from 06-05-2005 to 06-05-2022(seventeen years)with 4280 samples.As already noted,multiple studies have been performed to resolve this problem using linear regression,support vectormachines,deep long short-termmemory(LSTM),and many other models.In this research,the Hidden Markov Model(HMM)outperformed other employed machine learning ensembles,tree-based models,the ARIMA(Auto Regressive IntegratedMoving Average)model,and long short-term memory with a robust mean accuracy score of 99.98.Other statistical analyses and measurements for machine learning ensemble algorithms,the Long Short-TermModel,and ARIMA were also carried out for further investigation of the performance of advanced models for forecasting time series data.Thus,the proposed research found the best model to be HMM,and LSTM was the second-best model that performed well in all aspects.A developedmodel will be highly recommended and helpful for early measurement of technology stock performance for investment or withdrawal based on the future stock rise or fall for creating smart environments.展开更多
This paper reviews literature pertaining to the development of data science as a discipline,current issues with data bias and ethics,and the role that the discipline of information science may play in addressing these...This paper reviews literature pertaining to the development of data science as a discipline,current issues with data bias and ethics,and the role that the discipline of information science may play in addressing these concerns.Information science research and researchers have much to offer for data science,owing to their background as transdisciplinary scholars who apply human-centered and social-behavioral perspectives to issues within natural science disciplines.Information science researchers have already contributed to a humanistic approach to data ethics within the literature and an emphasis on data science within information schools all but ensures that this literature will continue to grow in coming decades.This review article serves as a reference for the history,current progress,and potential future directions of data ethics research within the corpus of information science literature.展开更多
In bioinformatics applications,examination of microarray data has received significant interest to diagnose diseases.Microarray gene expression data can be defined by a massive searching space that poses a primary cha...In bioinformatics applications,examination of microarray data has received significant interest to diagnose diseases.Microarray gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of genes.Microarray data classification incorporates multiple disciplines such as bioinformatics,machine learning(ML),data science,and pattern classification.This paper designs an optimal deep neural network based microarray gene expression classification(ODNN-MGEC)model for bioinformatics applications.The proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform scale.Besides,improved fruit fly optimization(IFFO)based feature selection technique is used to reduce the high dimensionality in the biomedical data.Moreover,deep neural network(DNN)model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search(SOS)algorithm.The utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification outcomes.For examining the improved outcomes of the ODNN-MGEC technique,a wide ranging experimental analysis is made against benchmark datasets.The extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures.展开更多
Introduction Within the field of scientometrics,which involves quantitative studies of science,the citation analysis specialism counts citations between academic papers in order to help evaluate the impact of the cite...Introduction Within the field of scientometrics,which involves quantitative studies of science,the citation analysis specialism counts citations between academic papers in order to help evaluate the impact of the cited work（Moed,2006）.展开更多
In this editorial,we comment on the current development and deployment of data science in intensive care units(ICUs).Data in ICUs can be classified into qualitative and quantitative data with different technologies ne...In this editorial,we comment on the current development and deployment of data science in intensive care units(ICUs).Data in ICUs can be classified into qualitative and quantitative data with different technologies needed to translate and interpret them.Data science,in the form of artificial intelligence(AI),should find the right interaction between physicians,data and algorithm.For individual patients and physicians,sepsis and mechanical ventilation have been two important aspects where AI has been extensively studied.However,major risks of bias,lack of generalizability and poor clinical values remain.AI deployment in the ICUs should be emphasized more to facilitate AI development.For ICU management,AI has a huge potential in transforming resource allocation.The coronavirus disease 2019 pandemic has given opportunities to establish such systems which should be investigated further.Ethical concerns must be addressed when designing such AI.展开更多
In order to conduct research and analysis on the construction of application-oriented undergraduate data science and big data technology courses,the professional development characteristics of universities and enterpr...In order to conduct research and analysis on the construction of application-oriented undergraduate data science and big data technology courses,the professional development characteristics of universities and enterprises should be taken into consideration,the development trend of the big data industry should be scrutinized,and professional application-oriented talents should be cultivated in line with job requirements.This paper expounds the demand for capacity-building professional development in application-oriented undergraduate data science and big data technology courses,conducts research and analysis on the current situation of professional development,and puts forward strategies in hope to provide reference for capacity-building professional development.展开更多
This article presents views on the future development of data science,with a particular focus on its importance to artificial intel-ligence(AI).After discussing the challenges of data science,it elu-cidates a possible...This article presents views on the future development of data science,with a particular focus on its importance to artificial intel-ligence(AI).After discussing the challenges of data science,it elu-cidates a possible approach to tackle these challenges by clarifying the logic and principles of data related to the multi-level complex-ity of the world.Finally,urgently required actions are briefly outlined.展开更多
Real-world clinical evaluation of traditional Chinese medicine(RWCE-TCM)is a method for comprehensively evaluating the clinical effects of TCM,with the aim of delving into the causality between TCM intervention and cl...Real-world clinical evaluation of traditional Chinese medicine(RWCE-TCM)is a method for comprehensively evaluating the clinical effects of TCM,with the aim of delving into the causality between TCM intervention and clinical outcomes.The study explored data science and causal learning methods to transform RWD into reliable real-world evidence,aiming to provide an innovative approach for RWCE-TCM.This study proposes a 10-step data science methodology to address the challenges posed by diverse and complex data in RWCE-TCM.The methodology involves several key steps,including data integration and warehouse building,high-dimensional feature selection,the use of interpretable statistical machine learning algorithms,complex networks,and graph network analysis,knowledge mining techniques such as natural language processing and machine learning,observational study design,and the application of artificial intelligence tools to build an intelligent engine for translational analysis.The goal is to establish a method for clinical positioning,applicable population screening,and mining the structural association of TCM characteristic therapies.In addition,the study adopts the principle of real-world research and a causal learning method for TCM clinical data.We constructed a multidimensional clinical knowledge map of“disease-syndrome-symptom-prescription-medicine”to enhance our understanding of the diagnosis and treatment laws of TCM,clarify the unique therapies,and explore information conducive to individualized treatment.The causal inference process of observational data can address confounding bias and reduce individual heterogeneity,promoting the transformation of TCM RWD into reliable clinical evidence.Intelligent data science improves efficiency and accuracy for implementing RWCE-TCM.The proposed data science methodology for TCM can handle complex data,ensure high-quality RWD acquisition and analysis,and provide in-depth insights into clinical benefits of TCM.This method supports the intelligent translation and demonstration of RWD in TCM,leads the data-driven translational analysis of causal learning,and innovates the path of RWCE-TCM.展开更多
Socioecological inequity in environmental data science—such as inequities deriving from data-driven approaches and machine learning(ML)—are current issues subject to debate and evolution.There is growing consensus a...Socioecological inequity in environmental data science—such as inequities deriving from data-driven approaches and machine learning(ML)—are current issues subject to debate and evolution.There is growing consensus around embedding equity throughout all research and design domains—from inception to administration,while also addressing procedural,distributive,and recognitional factors.Yet,practically doing so may seem onerous or daunting to some.The current perspective helps to alleviate these types of concerns by providing substantiation for the connection between environmental data science and socioecological inequity,using the Systemic Equity Framework,and provides the foundation for a paradigmatic shift toward normalizing the use of equity-centered approaches in environmental data science and ML settings.Bolstering the integrity of environmental data science and ML is just beginning from an equity-centered tool development and rigorous application standpoint.To this end,this perspective also provides relevant future directions and challenges by overviewing some meaningful tools and strategies—such as applying the Wells-Du Bois Protocol,employing fairness metrics,and systematically addressing irreproducibility;emerging needs and proposals—such as addressing data-proxy bias and supporting convergence research;and establishes a ten-step path forward.Afterall,the work that environmental scientists and engineers do ultimately affect the well-being of us all.展开更多
The entering into big data era gives rise to a novel discipline called Data Science.Data Science is interdisciplinary in its nature,and the existing relevant studies can be categorized into domain-independent studies ...The entering into big data era gives rise to a novel discipline called Data Science.Data Science is interdisciplinary in its nature,and the existing relevant studies can be categorized into domain-independent studies and domain-dependent studies.The domain-dependent studies and domain-independent ones are evolving into Domain-general Data Science and Domain-specific Data Science.Domain-general Data Science emphasizes Data Science in a general sense,involving concepts,theories,methods,technologies,and tools.Domain-specific Data Science is a variant of Domain-general Data Science and varies from one domain to another.The most popular Domain-specific Data Science includes Data journalism,Industrial Data Science,Business Data Science,Health Data Science,Biological Data Science,Social Data Science,and Agile Data Science.The difference between Domain-general Data Science and Domain-specific Data Science roots in their thinking paradigms:DGDS conforms to data-centered thinking,while DSDS is in line with knowledge-centered thinking.As a result,DGDS focuses on the theoretical studies,while DSDS is centered on applied ones.However,DSDS and DGDS possess complementary advantages.Theoretical Data Science(TDS)is a new branch of Data Science that employs mathematical models and abstractions of data objects and systems to rationalize,explain and predict big data phenomena.TDS will bridge the gap between DGDS and DSDS.TDS contrasts with DSDS,which uses casual analysis,as well as DGDS,which employs data-centered thinking to deal with big data problems in that it balances the usability and the interpretability of Data Science practices.The main concerns of TDS are concentrated on integrating the data-centered thinking with the knowledge-centered thinking as well as transforming a correlation analysis into the casual analysis.Hence,TDS can bridge the gaps between DGDS and DSDS,and balance the usability and the interpretability of big data solutions.The studies of TDS should be focused on the following research purpose:to develop theoretical studies of TDS,to take advantages of active property of big data,to embrace design of experiments,to enhance causality analysis,and to develop data products.展开更多
A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,...A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,social network)in the corresponding social-environmental systems(SES).To address these challenges,we need to understand decisions made and actions taken by agents,the outcomes of their actions,including the feedbacks on the corresponding agents and environment.The science of complex adaptive systems-complex adaptive sys tems(CAS)science-has a significant potential to handle such challenges.We address the advantages of CAS science for sustainability by identifying the key elements and challenges in sustainability science,the generic features of CAS,and the key advances and challenges in modeling CAS.Artificial intelligence and data science combined with agent-based modeling promise to improve understanding of agents’behaviors,detect SES struc tures,and formulate SES mechanisms.展开更多
Purpose–Data science is the study of the generalizable extraction of knowledge from data.It includes a variety of components and develops on methods and concepts from many domains,containing mathematics,probability m...Purpose–Data science is the study of the generalizable extraction of knowledge from data.It includes a variety of components and develops on methods and concepts from many domains,containing mathematics,probability models,machine learning,statistical learning,computer programming,data engineering,pattern recognition and learning,visualization and data warehousing aiming to extract value from data.The purpose of this paper is to provide an overview of open source(OS)data science tools,proposing a classification scheme that can be used to study OS data science software.Design/methodology/approach–The proposed classification scheme is based on general characteristics,project activity,operational characteristics and data mining characteristics.The authors then use the proposed scheme to examine 70 identified Open Source Software.From this the authors provide insight about the current status of OS data science tools and reveal the state-of-the-art tools.Findings–The features of 70 OS tools are recorded based on the criteria of the four group characteristics,general characteristics,project activity,operational characteristics and data mining characteristics.Interesting results came from the analysis of these features and are recorded here.Originality/value–The contribution of this survey is development of a new classification scheme for examination and study of OS data science tools.In parallel,this study provides an overview of existing OS data science tools.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 62371181in part by the Changzhou Science and Technology International Cooperation Program under Grant CZ20230029+1 种基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(2021R1A2B5B02087169)supported under the framework of international cooperation program managed by the National Research Foundation of Korea(2022K2A9A1A01098051)。
文摘The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate decisions.Data science is the science of dealing with data and its relationships through intelligent approaches.Most state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their integration.Therefore,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various characteristics.The paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based systems.Extensive insights into IoT data security,privacy,and challenges are visualized in the context of data science for IoT.In addition,this study reveals the current opportunities to enhance data science and IoT market development.The current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.
文摘With the rapid development of data science and artificial intelligence technology,its application in education in the field of extensive,which is of great significance to promote educational equity.By collecting and analyzing students’data,personalized learning provides customized learning path;the intelligent auxiliary education system provides personalized guidance to reduce the burden of teachers.This paper discusses the strategies of data science and artificial intelligence in promoting educational equity,including the establishment of a comprehensive student data collection and analysis system and the promotion of intelligent auxiliary education system,aiming to realize the optimal allocation of educational resources,so that every student can enjoy fair and high-quality education.
文摘Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and future jobs, and thus student careers. At the heart of this digital transformation is data science, the discipline that makes sense of big data. With many rapidly emerging digital challenges ahead of us, this article discusses perspectives on iSchools＇ opportunities and suggestions in data science education. We argue that iSchools should empower their students with ＂information computing＂ disciplines, which we define as the ability to solve problems and create values, information, and knowledge using tools in application domains. As specific approaches to enforcing information computing disciplines in data science education, we suggest the three foci of user-based, tool-based, and application- based. These three loci will serve to differentiate the data science education of iSchools from that of computer science or business schools. We present a layered Data Science Education Framework （DSEF） with building blocks that include the three pillars of data science （people, technology, and data）, computational thinking, data-driven paradigms, and data science lifecycles. Data science courses built on the top of this framework should thus be executed with user-based, tool-based, and application-based approaches. This framework will help our students think about data science problems from the big picture perspective and foster appropriate problem-solving skills in conjunction with broad perspectives of data science lifecycles. We hope the DSEF discussed in this article will help fellow iSchools in their design of new data science curricula.
文摘Purpose： The purpose of the paper is to provide a framework for addressing the disconnect between metadata and data science. Data science cannot progress without metadata research.This paper takes steps toward advancing the synergy between metadata and data science, and identifies pathways for developing a more cohesive metadata research agenda in data science. Design/methodology/approach： This paper identifies factors that challenge metadata research in the digital ecosystem, defines metadata and data science, and presents the concepts big metadata, smart metadata, and metadata capital as part of a metadata lingua franca connecting to data science. Findings： The ＂utilitarian nature＂ and ＂historical and traditional views＂ of metadata are identified as two intersecting factors that have inhibited metadata research. Big metadata, smart metadata, and metadata capital are presented as part ofa metadata linguafranca to help frame research in the data science research space. Research limitations： There are additional, intersecting factors to consider that likely inhibit metadata research, and other significant metadata concepts to explore. Practical implications： The immediate contribution of this work is that it may elicit response, critique, revision, or, more significantly, motivate research. The work presented can encourage more researchers to consider the significance of metadata as a research worthy topic within data science and the larger digital ecosystem. Originality/value： Although metadata research has not kept pace with other data science topics, there is little attention directed to this problem. This is surprising, given that metadata is essential for data science endeavors. This examination synthesizes original and prior scholarship to provide new grounding for metadata research in data science.
基金Project supported by the National Key R&D Program of China(Grant No.2016YFB0700503)the National High Technology Research and Development Program of China(Grant No.2015AA03420)+2 种基金Beijing Municipal Science and Technology Project,China(Grant No.D161100002416001)the National Natural Science Foundation of China(Grant No.51172018)Kennametal Inc
文摘Since its launch in 2011, the Materials Genome Initiative（MGI） has drawn the attention of researchers from academia,government, and industry worldwide. As one of the three tools of the MGI, the use of materials data, for the first time, has emerged as an extremely significant approach in materials discovery. Data science has been applied in different disciplines as an interdisciplinary field to extract knowledge from data. The concept of materials data science has been utilized to demonstrate its application in materials science. To explore its potential as an active research branch in the big data era, a three-tier system has been put forward to define the infrastructure for the classification, curation and knowledge extraction of materials data.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP 1/147/42),www.kku.edu.sa.This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-Track Path of Research Funding Program.
文摘In present digital era,data science techniques exploit artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to have an impact and develop their businesses.Data science integrates the conventions of econometrics with the technological elements of data science.It make use of machine learning(ML),predictive and prescriptive analytics to effectively understand financial data and solve related problems.Smart technologies for SMEs enable allows the firm to get smarter with their processes and offers efficient operations.At the same time,it is needed to develop an effective tool which can assist small to medium sized enterprises to forecast business failure as well as financial crisis.AI becomes a familiar tool for several businesses due to the fact that it concentrates on the design of intelligent decision making tools to solve particular real time problems.With this motivation,this paper presents a new AI based optimal functional link neural network(FLNN)based financial crisis prediction(FCP)model forSMEs.The proposed model involves preprocessing,feature selection,classification,and parameter tuning.At the initial stage,the financial data of the enterprises are collected and are preprocessed to enhance the quality of the data.Besides,a novel chaotic grasshopper optimization algorithm(CGOA)based feature selection technique is applied for the optimal selection of features.Moreover,functional link neural network(FLNN)model is employed for the classification of the feature reduced data.Finally,the efficiency of theFLNNmodel can be improvised by the use of cat swarm optimizer(CSO)algorithm.A detailed experimental validation process takes place on Polish dataset to ensure the performance of the presented model.The experimental studies demonstrated that the CGOA-FLNN-CSO model has accomplished maximum prediction accuracy of 98.830%,92.100%,and 95.220%on the applied Polish dataset Year I-III respectively.
文摘There has long been discussion about the distinctions of library science,information science,and informatics,and how these areas differ and overlap with computer science.Today the term data science is emerging that generates excitement and questions about how it relates to and differs from these other areas of study.
基金supported by Kyungpook National University Research Fund,2020.
文摘The rise or fall of the stock markets directly affects investors’interest and loyalty.Therefore,it is necessary to measure the performance of stocks in the market in advance to prevent our assets from suffering significant losses.In our proposed study,six supervised machine learning(ML)strategies and deep learning(DL)models with long short-term memory(LSTM)of data science was deployed for thorough analysis and measurement of the performance of the technology stocks.Under discussion are Apple Inc.(AAPL),Microsoft Corporation(MSFT),Broadcom Inc.,Taiwan Semiconductor Manufacturing Company Limited(TSM),NVIDIA Corporation(NVDA),and Avigilon Corporation(AVGO).The datasets were taken from the Yahoo Finance API from 06-05-2005 to 06-05-2022(seventeen years)with 4280 samples.As already noted,multiple studies have been performed to resolve this problem using linear regression,support vectormachines,deep long short-termmemory(LSTM),and many other models.In this research,the Hidden Markov Model(HMM)outperformed other employed machine learning ensembles,tree-based models,the ARIMA(Auto Regressive IntegratedMoving Average)model,and long short-term memory with a robust mean accuracy score of 99.98.Other statistical analyses and measurements for machine learning ensemble algorithms,the Long Short-TermModel,and ARIMA were also carried out for further investigation of the performance of advanced models for forecasting time series data.Thus,the proposed research found the best model to be HMM,and LSTM was the second-best model that performed well in all aspects.A developedmodel will be highly recommended and helpful for early measurement of technology stock performance for investment or withdrawal based on the future stock rise or fall for creating smart environments.
文摘This paper reviews literature pertaining to the development of data science as a discipline,current issues with data bias and ethics,and the role that the discipline of information science may play in addressing these concerns.Information science research and researchers have much to offer for data science,owing to their background as transdisciplinary scholars who apply human-centered and social-behavioral perspectives to issues within natural science disciplines.Information science researchers have already contributed to a humanistic approach to data ethics within the literature and an emphasis on data science within information schools all but ensures that this literature will continue to grow in coming decades.This review article serves as a reference for the history,current progress,and potential future directions of data ethics research within the corpus of information science literature.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/42/43)This work was supported by Taif University Researchers Supporting Program(project number:TURSP-2020/200),Taif University,Saudi Arabia.
文摘In bioinformatics applications,examination of microarray data has received significant interest to diagnose diseases.Microarray gene expression data can be defined by a massive searching space that poses a primary challenge in the appropriate selection of genes.Microarray data classification incorporates multiple disciplines such as bioinformatics,machine learning(ML),data science,and pattern classification.This paper designs an optimal deep neural network based microarray gene expression classification(ODNN-MGEC)model for bioinformatics applications.The proposed ODNN-MGEC technique performs data normalization process to normalize the data into a uniform scale.Besides,improved fruit fly optimization(IFFO)based feature selection technique is used to reduce the high dimensionality in the biomedical data.Moreover,deep neural network(DNN)model is applied for the classification of microarray gene expression data and the hyperparameter tuning of the DNN model is carried out using the Symbiotic Organisms Search(SOS)algorithm.The utilization of IFFO and SOS algorithms pave the way for accomplishing maximum gene expression classification outcomes.For examining the improved outcomes of the ODNN-MGEC technique,a wide ranging experimental analysis is made against benchmark datasets.The extensive comparison study with recent approaches demonstrates the enhanced outcomes of the ODNN-MGEC technique in terms of different measures.
文摘Introduction Within the field of scientometrics,which involves quantitative studies of science,the citation analysis specialism counts citations between academic papers in order to help evaluate the impact of the cited work（Moed,2006）.
文摘In this editorial,we comment on the current development and deployment of data science in intensive care units(ICUs).Data in ICUs can be classified into qualitative and quantitative data with different technologies needed to translate and interpret them.Data science,in the form of artificial intelligence(AI),should find the right interaction between physicians,data and algorithm.For individual patients and physicians,sepsis and mechanical ventilation have been two important aspects where AI has been extensively studied.However,major risks of bias,lack of generalizability and poor clinical values remain.AI deployment in the ICUs should be emphasized more to facilitate AI development.For ICU management,AI has a huge potential in transforming resource allocation.The coronavirus disease 2019 pandemic has given opportunities to establish such systems which should be investigated further.Ethical concerns must be addressed when designing such AI.
文摘In order to conduct research and analysis on the construction of application-oriented undergraduate data science and big data technology courses,the professional development characteristics of universities and enterprises should be taken into consideration,the development trend of the big data industry should be scrutinized,and professional application-oriented talents should be cultivated in line with job requirements.This paper expounds the demand for capacity-building professional development in application-oriented undergraduate data science and big data technology courses,conducts research and analysis on the current situation of professional development,and puts forward strategies in hope to provide reference for capacity-building professional development.
文摘This article presents views on the future development of data science,with a particular focus on its importance to artificial intel-ligence(AI).After discussing the challenges of data science,it elu-cidates a possible approach to tackle these challenges by clarifying the logic and principles of data related to the multi-level complex-ity of the world.Finally,urgently required actions are briefly outlined.
基金This work was funded by the scientific and technological innovation project of China Academy of Chinese Medical Sciences(CI2021A04706,CI2021B003)the National Key Research and Development Program of China(2023YFC3503404,2017YFC1700406-2,2018YFC1704306)the independent selection project of China Academy of Chinese Medical Sciences(Z0643,Z0723).
文摘Real-world clinical evaluation of traditional Chinese medicine(RWCE-TCM)is a method for comprehensively evaluating the clinical effects of TCM,with the aim of delving into the causality between TCM intervention and clinical outcomes.The study explored data science and causal learning methods to transform RWD into reliable real-world evidence,aiming to provide an innovative approach for RWCE-TCM.This study proposes a 10-step data science methodology to address the challenges posed by diverse and complex data in RWCE-TCM.The methodology involves several key steps,including data integration and warehouse building,high-dimensional feature selection,the use of interpretable statistical machine learning algorithms,complex networks,and graph network analysis,knowledge mining techniques such as natural language processing and machine learning,observational study design,and the application of artificial intelligence tools to build an intelligent engine for translational analysis.The goal is to establish a method for clinical positioning,applicable population screening,and mining the structural association of TCM characteristic therapies.In addition,the study adopts the principle of real-world research and a causal learning method for TCM clinical data.We constructed a multidimensional clinical knowledge map of“disease-syndrome-symptom-prescription-medicine”to enhance our understanding of the diagnosis and treatment laws of TCM,clarify the unique therapies,and explore information conducive to individualized treatment.The causal inference process of observational data can address confounding bias and reduce individual heterogeneity,promoting the transformation of TCM RWD into reliable clinical evidence.Intelligent data science improves efficiency and accuracy for implementing RWCE-TCM.The proposed data science methodology for TCM can handle complex data,ensure high-quality RWD acquisition and analysis,and provide in-depth insights into clinical benefits of TCM.This method supports the intelligent translation and demonstration of RWD in TCM,leads the data-driven translational analysis of causal learning,and innovates the path of RWCE-TCM.
基金the National Science Foundation of the USA for Facilitating Funded Network Building,Convergence Exploration,and Equity Concept Development(Nos.2115405,2241237,and 2115453)Each of these funded efforts were distinctly meaningful in the development of this perspective.
文摘Socioecological inequity in environmental data science—such as inequities deriving from data-driven approaches and machine learning(ML)—are current issues subject to debate and evolution.There is growing consensus around embedding equity throughout all research and design domains—from inception to administration,while also addressing procedural,distributive,and recognitional factors.Yet,practically doing so may seem onerous or daunting to some.The current perspective helps to alleviate these types of concerns by providing substantiation for the connection between environmental data science and socioecological inequity,using the Systemic Equity Framework,and provides the foundation for a paradigmatic shift toward normalizing the use of equity-centered approaches in environmental data science and ML settings.Bolstering the integrity of environmental data science and ML is just beginning from an equity-centered tool development and rigorous application standpoint.To this end,this perspective also provides relevant future directions and challenges by overviewing some meaningful tools and strategies—such as applying the Wells-Du Bois Protocol,employing fairness metrics,and systematically addressing irreproducibility;emerging needs and proposals—such as addressing data-proxy bias and supporting convergence research;and establishes a ten-step path forward.Afterall,the work that environmental scientists and engineers do ultimately affect the well-being of us all.
基金the Ministry of education of Humanities and Social Science project(Project No.20YJA870003)
文摘The entering into big data era gives rise to a novel discipline called Data Science.Data Science is interdisciplinary in its nature,and the existing relevant studies can be categorized into domain-independent studies and domain-dependent studies.The domain-dependent studies and domain-independent ones are evolving into Domain-general Data Science and Domain-specific Data Science.Domain-general Data Science emphasizes Data Science in a general sense,involving concepts,theories,methods,technologies,and tools.Domain-specific Data Science is a variant of Domain-general Data Science and varies from one domain to another.The most popular Domain-specific Data Science includes Data journalism,Industrial Data Science,Business Data Science,Health Data Science,Biological Data Science,Social Data Science,and Agile Data Science.The difference between Domain-general Data Science and Domain-specific Data Science roots in their thinking paradigms:DGDS conforms to data-centered thinking,while DSDS is in line with knowledge-centered thinking.As a result,DGDS focuses on the theoretical studies,while DSDS is centered on applied ones.However,DSDS and DGDS possess complementary advantages.Theoretical Data Science(TDS)is a new branch of Data Science that employs mathematical models and abstractions of data objects and systems to rationalize,explain and predict big data phenomena.TDS will bridge the gap between DGDS and DSDS.TDS contrasts with DSDS,which uses casual analysis,as well as DGDS,which employs data-centered thinking to deal with big data problems in that it balances the usability and the interpretability of Data Science practices.The main concerns of TDS are concentrated on integrating the data-centered thinking with the knowledge-centered thinking as well as transforming a correlation analysis into the casual analysis.Hence,TDS can bridge the gaps between DGDS and DSDS,and balance the usability and the interpretability of big data solutions.The studies of TDS should be focused on the following research purpose:to develop theoretical studies of TDS,to take advantages of active property of big data,to embrace design of experiments,to enhance causality analysis,and to develop data products.
基金The National Science Foundation funded this research under the Dy-namics of Coupled Natural and Human Systems program(Grants No.DEB-1212183 and BCS-1826839)support from San Diego State University and Auburn University.
文摘A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,social network)in the corresponding social-environmental systems(SES).To address these challenges,we need to understand decisions made and actions taken by agents,the outcomes of their actions,including the feedbacks on the corresponding agents and environment.The science of complex adaptive systems-complex adaptive sys tems(CAS)science-has a significant potential to handle such challenges.We address the advantages of CAS science for sustainability by identifying the key elements and challenges in sustainability science,the generic features of CAS,and the key advances and challenges in modeling CAS.Artificial intelligence and data science combined with agent-based modeling promise to improve understanding of agents’behaviors,detect SES struc tures,and formulate SES mechanisms.
基金The research leading to the results presented in this paper has received funding from the European Union Seventh Framework Programme(FP7-2012-NMP-ICT-FoF)under Grant Agreement No.314364.
文摘Purpose–Data science is the study of the generalizable extraction of knowledge from data.It includes a variety of components and develops on methods and concepts from many domains,containing mathematics,probability models,machine learning,statistical learning,computer programming,data engineering,pattern recognition and learning,visualization and data warehousing aiming to extract value from data.The purpose of this paper is to provide an overview of open source(OS)data science tools,proposing a classification scheme that can be used to study OS data science software.Design/methodology/approach–The proposed classification scheme is based on general characteristics,project activity,operational characteristics and data mining characteristics.The authors then use the proposed scheme to examine 70 identified Open Source Software.From this the authors provide insight about the current status of OS data science tools and reveal the state-of-the-art tools.Findings–The features of 70 OS tools are recorded based on the criteria of the four group characteristics,general characteristics,project activity,operational characteristics and data mining characteristics.Interesting results came from the analysis of these features and are recorded here.Originality/value–The contribution of this survey is development of a new classification scheme for examination and study of OS data science tools.In parallel,this study provides an overview of existing OS data science tools.
