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 e...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 deep neural networks(DNNs),and algorithms such as Naive Bayes 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.展开更多
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 injec...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.展开更多
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...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.展开更多
The primary motivation for this study is the recent growth and increased interest in artificial intelligence(AI).Despite the widespread recognition of its critical importance,a discernible scientific gap persists with...The primary motivation for this study is the recent growth and increased interest in artificial intelligence(AI).Despite the widespread recognition of its critical importance,a discernible scientific gap persists within the extant scholarly discourse,particularly concerning exhaustive systematic reviews of AI in the aviation industry.This gap spurred a meticulous analysis of 1,213 articles from the Web of Science(WoS)core database for bibliometric knowledge mapping.This analysis highlights China as the primary contributor to publications,with the Nanjing University of Finance and Economics as the leading institution in paper contributions.Lecture Notes in Artificial Intelligence and the IEEE AIAA Digital Avionics System Conference are the leading journals within this domain.This bibliometric research underscores the key focus on air traffic management,human factors,environmental ini-tiatives,training,logistics,flight operations,and safety through co-occurrence and co-citation analyses.A chro-nological examination of keywords reveals a central research trajectory centered on machine learning,models,deep learning,and the impact of automation on human performance in aviation.Burst keyword analysis identifies the leading-edge research on AI within predictive models,unmanned aerial vehicles,object detection,and con-volutional neural networks.The primary objective is to bridge this knowledge gap and gain comprehensive in-sights into AI in the aviation sector.This study delineates the scholarly terrain of AI in aviation using a bibliometric methodology to facilitate this exploration.The results illuminate the current state of research,thereby enhancing academic understanding of developments within this critical domain.Finally,a new con-ceptual framework was constructed based on the primary elements identified in the literature.This framework can assist emerging researchers in identifying the fundamental dimensions of AI in the aviation industry.展开更多
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 ...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 3,900 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.展开更多
Public-and private-sector organizations have adopted artificial intelligence(AI)to meet the challenges of the Fourth Industrial Revolution.The successful implementation of AI is a challenging task,and previous researc...Public-and private-sector organizations have adopted artificial intelligence(AI)to meet the challenges of the Fourth Industrial Revolution.The successful implementation of AI is a challenging task,and previous research has advocated the need to explore key readiness before AI implementation.The objective of this study is to identify the AI readiness factors explored by different authors in past research.To achieve this,we conducted a rigorous literature review.The approach used in the systematic literature review is also discussed.A rigorous review of 52 studies from various journals and databases(Science Direct,Springer Link,Institute of Electrical and Electronics Engineers,Emerald,and Google Scholar)identified 23 AI readiness factors.The key factors identified were mainly related to organizational information technology infrastructure,top management support,resource availability,collaborative culture,organizational size,organizational capability,compatibility,data quality,and financial budget,whereas the other 15 were potential factors in AI readiness.All of these factors should be considered before the implementation of AI in any organization.The findings also reflect a high failure rate,including AI readiness factors,which are intended to facilitate AI adoption in organizations and reduce the frequency of failures.These factors will aid management in developing an effective strategy for AI implementation in organizations.展开更多
In the rapidly evolving landscape of digital health,the integration of data analytics and Internet healthserviceshasbecome a pivotal area of exploration.To meet keen social needs,Prof.Shan Liu(Xi'an Jiaotong Unive...In the rapidly evolving landscape of digital health,the integration of data analytics and Internet healthserviceshasbecome a pivotal area of exploration.To meet keen social needs,Prof.Shan Liu(Xi'an Jiaotong University)and Prof.Xing Zhang(Wuhan Textile University)have published the timely book Datadriven Internet Health Platform Service Value Co-creation through China Science Press.The book focuses on the provision of medical and health services from doctors to patients through Internet health platforms,where the service value is co-created by three parties.展开更多
Types of paper Contributions falling into the following categories will be considered for publication:original research papers,review articles,short communications,technical notes,editorials,book reviews,case reports,...Types of paper Contributions falling into the following categories will be considered for publication:original research papers,review articles,short communications,technical notes,editorials,book reviews,case reports,and feature articles.Please ensure that you select the appropriate article type from the list of options when making your submission.Authors contributing to special issues should ensure that they select the special issue article type from the"types of article"list.展开更多
The editors regret that the following statements were missing in the published version for the following articles that appeared in previous issues of Data Science and Management:1.“Audiovisual speech recognition base...The editors regret that the following statements were missing in the published version for the following articles that appeared in previous issues of Data Science and Management:1.“Audiovisual speech recognition based on a deep convolutional neural network”(Data Science and Management,2024,7(1):25–34).https://doi.org/10.1016/j.dsm.2023.10.002.Ethics statement:The authors declare the Institutional Ethics Committee confirmed that no ethical review was required for this study.The authors have taken the participants’permission and consent to participate in this study.展开更多
Emotion recognition from electroencephalogram(EEG)signals has garnered significant attention owing to its potential applications in affective computing,human-computer interaction,and mental health monitoring.This pape...Emotion recognition from electroencephalogram(EEG)signals has garnered significant attention owing to its potential applications in affective computing,human-computer interaction,and mental health monitoring.This paper presents a comparative analysis of different machine learning methods for emotion recognition using EEG data.The objective of this study was to identify the most effective algorithm for accurately classifying emotional states using EEG signals.The EEG brainwave dataset:Feeling emotions dataset was used to evaluate the performance of various machine-learning techniques.Multiple machine learning techniques,namely logistic regression(LR),support vector machine(SVM),Gaussian Naive Bayes(GNB),and decision tree(DT),and ensemble models,namely random forest(RF),AdaBoost,LightGBM,XGBoost,and CatBoost,were trained and evaluated.Five-fold cross-validation and dimension reduction techniques,such as principal component analysis,tdistributed stochastic neighbor embedding,and linear discriminant analysis,were performed for all models.The least-performing model,GNB,showed substantially increased performance after dimension reduction.Performance metrics such as accuracy,precision,recall,F1-score,and receiver operating characteristic curves are employed to assess the effectiveness of each approach.This study focuses on the implications of using various machine learning algorithms for EEG-based emotion recognition.This pursuit can improve our understanding of emotions and their underlying neural mechanisms.展开更多
Types of paper Contributions falling into the following categories will be considered for publication:original research papers,review articles,short communications,technical notes,editorials,book reviews,case reports,...Types of paper Contributions falling into the following categories will be considered for publication:original research papers,review articles,short communications,technical notes,editorials,book reviews,case reports,and feature articles.Please ensure that you select the appropriate article type from the list of options when making your submission.Authors contributing to special issues should ensure that they select the special issue article type from the"types of article"list.展开更多
This study presents a methodology to enhance energy management systems(EMS)in hybrid electric vehicles(HEVs)to reduce fuel consumption and greenhouse gas emissions.A novel surrogate-assisted optimization framework is ...This study presents a methodology to enhance energy management systems(EMS)in hybrid electric vehicles(HEVs)to reduce fuel consumption and greenhouse gas emissions.A novel surrogate-assisted optimization framework is employed,incorporating key performance metrics such as fuel efficiency and emissions to develop data-driven surrogate models of the EMS.These models are optimized using various algorithms targeting parameters such as engine idle speed,thermostat temperature fraction,regeneration load factor,and battery stateof-charge thresholds.Correlation analysis highlights the significant impact of the lower state-of-charge threshold and thermostat temperature fraction on fuel efficiency and emissions.Among the optimization methods,the combination of a backpropagation neural network(BPNN)and a multi-objective genetic algorithm(MOGA)proves most effective,achieving fuel consumption reductions of 5.26%and 5.01%in charge-sustaining and charge-depletion modes,respectively.Additionally,the BPNN-based MOGA demonstrates notable improvements in emission reduction.These findings suggest that optimizing rule-based EMS parameters without altering underlying management rules can significantly enhance performance under diverse and unanticipated driving conditions.展开更多
In the burgeoning field of anomaly detection within attributed networks,traditional methodologies often encounter the intricacies of network complexity,particularly in capturing nonlinearity and sparsity.This study in...In the burgeoning field of anomaly detection within attributed networks,traditional methodologies often encounter the intricacies of network complexity,particularly in capturing nonlinearity and sparsity.This study introduces an innovative approach that synergizes the strengths of graph convolutional networks with advanced deep residual learning and a unique residual-based attention mechanism,thereby creating a more nuanced and efficient method for anomaly detection in complex networks.The heart of our model lies in the integration of graph convolutional networks that capture complex structural relationships within the network data.This is further bolstered by deep residual learning,which is employed to model intricate nonlinear connections directly from input data.A pivotal innovation in our approach is the incorporation of a residual-based attention mech-anism.This mechanism dynamically adjusts the importance of nodes based on their residual information,thereby significantly enhancing the sensitivity of the model to subtle anomalies.Furthermore,we introduce a novel hypersphere mapping technique in the latent space to distinctly separate normal and anomalous data.This mapping is the key to our model’s ability to pinpoint anomalies with greater precision.An extensive experimental setup was used to validate the efficacy of the proposed model.Using attributed social network datasets,we demonstrate that our model not only competes with but also surpasses existing state-of-the-art methods in anomaly detection.The results show the exceptional capability of our model to handle the multifaceted nature of real-world networks.展开更多
Against the backdrop of increasingly prominent global environmental issues,heavily polluting enterprises(HPPs)urgently need to find a path to green transformation that achieves sustainable development and overcomes ef...Against the backdrop of increasingly prominent global environmental issues,heavily polluting enterprises(HPPs)urgently need to find a path to green transformation that achieves sustainable development and overcomes efficiency challenges.Based on data on mergers and acquisitions of HPPs from 2010 to 2023,this article explores the direct impact and mechanisms of green mergers and acquisitions(GMAs)on enterprises green transformation.Research findings are as follows:(1)GMAs significantly promote the green transformation of HPPs,a conclusion that is robust across various tests.(2)Internal control and green innovation quality serve as partial and chain mediators,respectively,in the relationship between GMAs and the green transformation of HPPs.(3)Media pressure negatively affects the impact of GMAs on internal control.(4)The heterogeneity analysis shows that the GMAs of enterprises in the eastern region,non-state-owned enterprises,large enterprises,and enterprises in the electricity,heat,gas,and water production and supply industries have a more obvious impact on green transformation.These findings elucidate the mechanisms through which GMAs drive the green transformation of HPPs and offer empirical insights into supporting the sustainable development of such enterprises in China.展开更多
The identifiability of users as they interact in the digital world is fundamentally linked to privacy and security issues.Identifiability can be divided into two:subjective identifiability,which is based on psychologi...The identifiability of users as they interact in the digital world is fundamentally linked to privacy and security issues.Identifiability can be divided into two:subjective identifiability,which is based on psychological perceptions(i.e.,mental space),and objective identifiability,which is based on social media data(i.e.,information space).This study constructs a prediction model for social media data identifiability of users based on a supervised machine learning technique.The findings,based on data from Weibo,a Chinese social media platform,indicate that the top seven features and values for predicting social media identifiability include blog pictures(0.21),blog location(0.14),birthdate(0.12),location(0.10),blog interaction(0.10),school(0.08),and interests and hobbies(0.07).The relationship between machine-predicted and self-reported identifiability was tested using data from 91 participants.Based on the degree of deviation between the two,users can be divided into four categories—normal,conservative,active,and atypical—which reflect their sensitivity to privacy concerns and preferences regarding information disclosure.This study provides insights into the development of privacy protection strategies based on social media data classification.展开更多
In the health field,longitudinal studies involve the recording of clinical observations of the same sample of pa-tients over successive periods,referred to as waves.This type of database serves as a valuable source of...In the health field,longitudinal studies involve the recording of clinical observations of the same sample of pa-tients over successive periods,referred to as waves.This type of database serves as a valuable source of infor-mation and insights,particularly when examining the temporal aspect,allowing the extraction of relevant and non-obvious knowledge.The triadic concept analysis theory has been proposed to describe the ternary re-lationships between objects,attributes,and conditions.In this study,we present a methodology for exploring longitudinal health databases using both the triadic theory and triadic rules,which are similar to association rules but incorporate temporal relations.Through four case studies,we demonstrate the potential of applying triadic analysis to longitudinal databases to identify risk patterns,enhance decision-making processes,and deepen our understanding of temporal dynamics.These findings suggest a promising approach for describing longitudinal databases and obtaining insights to improve clinical decision-support systems for disease treatment.展开更多
Skin cancer is the abnormal development of cells on the surface of the skin and is one of the most fatal diseases in humans.It usually appears in locations that are exposed to the sun,but can also appear in areas that...Skin cancer is the abnormal development of cells on the surface of the skin and is one of the most fatal diseases in humans.It usually appears in locations that are exposed to the sun,but can also appear in areas that are not regularly exposed to the sun.Due to the striking similarities between benign and malignant lesions,skin cancer detection remains a problem,even for expert dermatologists.Considering the inability of dermatologists to di-agnose skin cancer accurately,a convolutional neural network(CNN)approach was used for skin cancer diag-nosis.However,the CNN model requires a significant number of image datasets for better performance;thus,image augmentation and transfer learning techniques have been used in this study to boost the number of images and the performance of the model,because there are a limited number of medical images.This study proposes an ensemble transfer-learning-based model that can efficiently classify skin lesions into one of seven categories to aid dermatologists in skin cancer detection:(i)actinic keratoses,(ii)basal cell carcinoma,(iii)benign keratosis,(iv)dermatofibroma,(v)melanocytic nevi,(vi)melanoma,and(vii)vascular skin lesions.Five transfer learning models were used as the basis of the ensemble:MobileNet,EfficientNetV2B2,Xception,ResNeXt101,and Den-seNet201.In addition to the stratified 10-fold cross-validation,the results of each individual model were fused to achieve greater classification accuracy.An annealing learning rate scheduler and test time augmentation(TTA)were also used to increase the performance of the model during the training and testing stages.A total of 10,015 publicly available dermoscopy images from the HAM10000(Human Against Machine)dataset,which contained samples from the seven common skin lesion categories,were used to train and evaluate the models.The proposed technique attained 94.49%accuracy on the dataset.These results suggest that this strategy can be useful for improving the accuracy of skin cancer classification.However,the weighted average of F1-score,recall,and precision were obtained to be 94.68%,94.49%,and 95.07%,respectively.展开更多
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 asp...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.展开更多
This paper presents and describes an approach to generate innovative labeled datasets that enable automated text classifiers to automatically detect online employee reviews referring to accounting control deficiencies...This paper presents and describes an approach to generate innovative labeled datasets that enable automated text classifiers to automatically detect online employee reviews referring to accounting control deficiencies,facili-tating supplementary monitoring for auditors and management.Employees,who are on the front lines executing policies and procedures,play a critical role in a firm's control environment.Their feedback provides insights into how controls are functioning.Textual data were collected and manually coded using a structured coding scheme mapped to COSO internal control framework(2013)principles.The dataset is unique in that it provides a new source of data that has not been previously used in internal control research,offering new opportunities for exploring the relationship between employee feedback and control weaknesses,and shedding light on potential improvements in internal control practices.Downstream stakeholders(such as researchers,management,in-vestors,and auditors)can benefit by having rapid,automated means for filtering and prioritizing employee reviews for further investigation,with respect to accounting control issue mentions.展开更多
Although generative conversational artificial intelligence(AI)can answer questions well and hold conversations as a person,the semantic ambiguity inherent in text-based communication poses challenges to effective use....Although generative conversational artificial intelligence(AI)can answer questions well and hold conversations as a person,the semantic ambiguity inherent in text-based communication poses challenges to effective use.Effective use reflects the users’utilization of generative conversational AI to achieve their goals,which has not been previously studied.Drawing on the media naturalness theory,we examined how generative conversational AI’s content and style naturalness affect effective use.A two-wave survey was conducted to collect data from 565 users of generative conversational AI.Two techniques were used in this study.Initially,partial least squares structural equation modeling(PLS-SEM)was applied to determine the variables that significantly affected the mechanisms(i.e.,cognitive effort and communication ambiguity)and effective use.Secondly,an artificial neural network model was used to evaluate the relative importance of the significant predictors of mechanisms and effective use identified from the PLS-SEM analysis.The results revealed that the naturalness of content and style differed in their effects on cognitive effort and communication ambiguity.Additionally,cognitive effort and communication ambiguity negatively affected effective use.This study advances the literature on effective use by uncovering the psychological mechanisms underlying effective use and their antecedents.In addition,this study offers insights into the design of generative conversational AI.展开更多
文摘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 deep neural networks(DNNs),and algorithms such as Naive Bayes 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.
文摘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.
基金the National Council for Scientific and Technological Development of Brazil(CNPQ)the Coordination for the Improvement of Higher Education Personnel-Brazil(CAPES)(Grant PROAP 88887.842889/2023-00-PUC/MG,Grant PDPG 88887.708960/2022-00-PUC/MG-INFORMATICA and Finance Code 001)Minas Gerais State Research Support Foundation(FAPEMIG)under Grant No.:APQ-01929-22,and the Pontifical Catholic University of Minas Gerais,Brazil.
文摘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.
文摘The primary motivation for this study is the recent growth and increased interest in artificial intelligence(AI).Despite the widespread recognition of its critical importance,a discernible scientific gap persists within the extant scholarly discourse,particularly concerning exhaustive systematic reviews of AI in the aviation industry.This gap spurred a meticulous analysis of 1,213 articles from the Web of Science(WoS)core database for bibliometric knowledge mapping.This analysis highlights China as the primary contributor to publications,with the Nanjing University of Finance and Economics as the leading institution in paper contributions.Lecture Notes in Artificial Intelligence and the IEEE AIAA Digital Avionics System Conference are the leading journals within this domain.This bibliometric research underscores the key focus on air traffic management,human factors,environmental ini-tiatives,training,logistics,flight operations,and safety through co-occurrence and co-citation analyses.A chro-nological examination of keywords reveals a central research trajectory centered on machine learning,models,deep learning,and the impact of automation on human performance in aviation.Burst keyword analysis identifies the leading-edge research on AI within predictive models,unmanned aerial vehicles,object detection,and con-volutional neural networks.The primary objective is to bridge this knowledge gap and gain comprehensive in-sights into AI in the aviation sector.This study delineates the scholarly terrain of AI in aviation using a bibliometric methodology to facilitate this exploration.The results illuminate the current state of research,thereby enhancing academic understanding of developments within this critical domain.Finally,a new con-ceptual framework was constructed based on the primary elements identified in the literature.This framework can assist emerging researchers in identifying the fundamental dimensions of AI in the aviation industry.
基金the National Natural Science Foundation of China(Grant No.:71771061).
文摘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 3,900 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.
文摘Public-and private-sector organizations have adopted artificial intelligence(AI)to meet the challenges of the Fourth Industrial Revolution.The successful implementation of AI is a challenging task,and previous research has advocated the need to explore key readiness before AI implementation.The objective of this study is to identify the AI readiness factors explored by different authors in past research.To achieve this,we conducted a rigorous literature review.The approach used in the systematic literature review is also discussed.A rigorous review of 52 studies from various journals and databases(Science Direct,Springer Link,Institute of Electrical and Electronics Engineers,Emerald,and Google Scholar)identified 23 AI readiness factors.The key factors identified were mainly related to organizational information technology infrastructure,top management support,resource availability,collaborative culture,organizational size,organizational capability,compatibility,data quality,and financial budget,whereas the other 15 were potential factors in AI readiness.All of these factors should be considered before the implementation of AI in any organization.The findings also reflect a high failure rate,including AI readiness factors,which are intended to facilitate AI adoption in organizations and reduce the frequency of failures.These factors will aid management in developing an effective strategy for AI implementation in organizations.
文摘In the rapidly evolving landscape of digital health,the integration of data analytics and Internet healthserviceshasbecome a pivotal area of exploration.To meet keen social needs,Prof.Shan Liu(Xi'an Jiaotong University)and Prof.Xing Zhang(Wuhan Textile University)have published the timely book Datadriven Internet Health Platform Service Value Co-creation through China Science Press.The book focuses on the provision of medical and health services from doctors to patients through Internet health platforms,where the service value is co-created by three parties.
文摘Types of paper Contributions falling into the following categories will be considered for publication:original research papers,review articles,short communications,technical notes,editorials,book reviews,case reports,and feature articles.Please ensure that you select the appropriate article type from the list of options when making your submission.Authors contributing to special issues should ensure that they select the special issue article type from the"types of article"list.
文摘The editors regret that the following statements were missing in the published version for the following articles that appeared in previous issues of Data Science and Management:1.“Audiovisual speech recognition based on a deep convolutional neural network”(Data Science and Management,2024,7(1):25–34).https://doi.org/10.1016/j.dsm.2023.10.002.Ethics statement:The authors declare the Institutional Ethics Committee confirmed that no ethical review was required for this study.The authors have taken the participants’permission and consent to participate in this study.
文摘Emotion recognition from electroencephalogram(EEG)signals has garnered significant attention owing to its potential applications in affective computing,human-computer interaction,and mental health monitoring.This paper presents a comparative analysis of different machine learning methods for emotion recognition using EEG data.The objective of this study was to identify the most effective algorithm for accurately classifying emotional states using EEG signals.The EEG brainwave dataset:Feeling emotions dataset was used to evaluate the performance of various machine-learning techniques.Multiple machine learning techniques,namely logistic regression(LR),support vector machine(SVM),Gaussian Naive Bayes(GNB),and decision tree(DT),and ensemble models,namely random forest(RF),AdaBoost,LightGBM,XGBoost,and CatBoost,were trained and evaluated.Five-fold cross-validation and dimension reduction techniques,such as principal component analysis,tdistributed stochastic neighbor embedding,and linear discriminant analysis,were performed for all models.The least-performing model,GNB,showed substantially increased performance after dimension reduction.Performance metrics such as accuracy,precision,recall,F1-score,and receiver operating characteristic curves are employed to assess the effectiveness of each approach.This study focuses on the implications of using various machine learning algorithms for EEG-based emotion recognition.This pursuit can improve our understanding of emotions and their underlying neural mechanisms.
文摘Types of paper Contributions falling into the following categories will be considered for publication:original research papers,review articles,short communications,technical notes,editorials,book reviews,case reports,and feature articles.Please ensure that you select the appropriate article type from the list of options when making your submission.Authors contributing to special issues should ensure that they select the special issue article type from the"types of article"list.
文摘This study presents a methodology to enhance energy management systems(EMS)in hybrid electric vehicles(HEVs)to reduce fuel consumption and greenhouse gas emissions.A novel surrogate-assisted optimization framework is employed,incorporating key performance metrics such as fuel efficiency and emissions to develop data-driven surrogate models of the EMS.These models are optimized using various algorithms targeting parameters such as engine idle speed,thermostat temperature fraction,regeneration load factor,and battery stateof-charge thresholds.Correlation analysis highlights the significant impact of the lower state-of-charge threshold and thermostat temperature fraction on fuel efficiency and emissions.Among the optimization methods,the combination of a backpropagation neural network(BPNN)and a multi-objective genetic algorithm(MOGA)proves most effective,achieving fuel consumption reductions of 5.26%and 5.01%in charge-sustaining and charge-depletion modes,respectively.Additionally,the BPNN-based MOGA demonstrates notable improvements in emission reduction.These findings suggest that optimizing rule-based EMS parameters without altering underlying management rules can significantly enhance performance under diverse and unanticipated driving conditions.
文摘In the burgeoning field of anomaly detection within attributed networks,traditional methodologies often encounter the intricacies of network complexity,particularly in capturing nonlinearity and sparsity.This study introduces an innovative approach that synergizes the strengths of graph convolutional networks with advanced deep residual learning and a unique residual-based attention mechanism,thereby creating a more nuanced and efficient method for anomaly detection in complex networks.The heart of our model lies in the integration of graph convolutional networks that capture complex structural relationships within the network data.This is further bolstered by deep residual learning,which is employed to model intricate nonlinear connections directly from input data.A pivotal innovation in our approach is the incorporation of a residual-based attention mech-anism.This mechanism dynamically adjusts the importance of nodes based on their residual information,thereby significantly enhancing the sensitivity of the model to subtle anomalies.Furthermore,we introduce a novel hypersphere mapping technique in the latent space to distinctly separate normal and anomalous data.This mapping is the key to our model’s ability to pinpoint anomalies with greater precision.An extensive experimental setup was used to validate the efficacy of the proposed model.Using attributed social network datasets,we demonstrate that our model not only competes with but also surpasses existing state-of-the-art methods in anomaly detection.The results show the exceptional capability of our model to handle the multifaceted nature of real-world networks.
基金supported by the National Natural Science Foundation of China“Reconstruction of Competitive Advantage of China's High-tech Industry from the Perspective of Dual Value Chain”(Grant No.71972063)Natural Science Foundation of Heilongjiang Province“Innovation Decision-making and Performance of Green Factories in Heilongjiang Province under the Dual Carbon Target:an Incentive Environmental Regulation Perspective”(Grant No.JJ2022LH0765)Key R&D Program(Soft Science Project)of Shandong Province,China“Research on the Development Status and Countermeasures of High-tech Enterprises in Shandong Province”(Grant No.2023RZB03024).
文摘Against the backdrop of increasingly prominent global environmental issues,heavily polluting enterprises(HPPs)urgently need to find a path to green transformation that achieves sustainable development and overcomes efficiency challenges.Based on data on mergers and acquisitions of HPPs from 2010 to 2023,this article explores the direct impact and mechanisms of green mergers and acquisitions(GMAs)on enterprises green transformation.Research findings are as follows:(1)GMAs significantly promote the green transformation of HPPs,a conclusion that is robust across various tests.(2)Internal control and green innovation quality serve as partial and chain mediators,respectively,in the relationship between GMAs and the green transformation of HPPs.(3)Media pressure negatively affects the impact of GMAs on internal control.(4)The heterogeneity analysis shows that the GMAs of enterprises in the eastern region,non-state-owned enterprises,large enterprises,and enterprises in the electricity,heat,gas,and water production and supply industries have a more obvious impact on green transformation.These findings elucidate the mechanisms through which GMAs drive the green transformation of HPPs and offer empirical insights into supporting the sustainable development of such enterprises in China.
基金supported by the National Social Science Funds of China(Grant No.21BSH050)Major Project of National Social Science Funds of China(Grant No.20&ZD013).
文摘The identifiability of users as they interact in the digital world is fundamentally linked to privacy and security issues.Identifiability can be divided into two:subjective identifiability,which is based on psychological perceptions(i.e.,mental space),and objective identifiability,which is based on social media data(i.e.,information space).This study constructs a prediction model for social media data identifiability of users based on a supervised machine learning technique.The findings,based on data from Weibo,a Chinese social media platform,indicate that the top seven features and values for predicting social media identifiability include blog pictures(0.21),blog location(0.14),birthdate(0.12),location(0.10),blog interaction(0.10),school(0.08),and interests and hobbies(0.07).The relationship between machine-predicted and self-reported identifiability was tested using data from 91 participants.Based on the degree of deviation between the two,users can be divided into four categories—normal,conservative,active,and atypical—which reflect their sensitivity to privacy concerns and preferences regarding information disclosure.This study provides insights into the development of privacy protection strategies based on social media data classification.
文摘In the health field,longitudinal studies involve the recording of clinical observations of the same sample of pa-tients over successive periods,referred to as waves.This type of database serves as a valuable source of infor-mation and insights,particularly when examining the temporal aspect,allowing the extraction of relevant and non-obvious knowledge.The triadic concept analysis theory has been proposed to describe the ternary re-lationships between objects,attributes,and conditions.In this study,we present a methodology for exploring longitudinal health databases using both the triadic theory and triadic rules,which are similar to association rules but incorporate temporal relations.Through four case studies,we demonstrate the potential of applying triadic analysis to longitudinal databases to identify risk patterns,enhance decision-making processes,and deepen our understanding of temporal dynamics.These findings suggest a promising approach for describing longitudinal databases and obtaining insights to improve clinical decision-support systems for disease treatment.
文摘Skin cancer is the abnormal development of cells on the surface of the skin and is one of the most fatal diseases in humans.It usually appears in locations that are exposed to the sun,but can also appear in areas that are not regularly exposed to the sun.Due to the striking similarities between benign and malignant lesions,skin cancer detection remains a problem,even for expert dermatologists.Considering the inability of dermatologists to di-agnose skin cancer accurately,a convolutional neural network(CNN)approach was used for skin cancer diag-nosis.However,the CNN model requires a significant number of image datasets for better performance;thus,image augmentation and transfer learning techniques have been used in this study to boost the number of images and the performance of the model,because there are a limited number of medical images.This study proposes an ensemble transfer-learning-based model that can efficiently classify skin lesions into one of seven categories to aid dermatologists in skin cancer detection:(i)actinic keratoses,(ii)basal cell carcinoma,(iii)benign keratosis,(iv)dermatofibroma,(v)melanocytic nevi,(vi)melanoma,and(vii)vascular skin lesions.Five transfer learning models were used as the basis of the ensemble:MobileNet,EfficientNetV2B2,Xception,ResNeXt101,and Den-seNet201.In addition to the stratified 10-fold cross-validation,the results of each individual model were fused to achieve greater classification accuracy.An annealing learning rate scheduler and test time augmentation(TTA)were also used to increase the performance of the model during the training and testing stages.A total of 10,015 publicly available dermoscopy images from the HAM10000(Human Against Machine)dataset,which contained samples from the seven common skin lesion categories,were used to train and evaluate the models.The proposed technique attained 94.49%accuracy on the dataset.These results suggest that this strategy can be useful for improving the accuracy of skin cancer classification.However,the weighted average of F1-score,recall,and precision were obtained to be 94.68%,94.49%,and 95.07%,respectively.
文摘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.
文摘This paper presents and describes an approach to generate innovative labeled datasets that enable automated text classifiers to automatically detect online employee reviews referring to accounting control deficiencies,facili-tating supplementary monitoring for auditors and management.Employees,who are on the front lines executing policies and procedures,play a critical role in a firm's control environment.Their feedback provides insights into how controls are functioning.Textual data were collected and manually coded using a structured coding scheme mapped to COSO internal control framework(2013)principles.The dataset is unique in that it provides a new source of data that has not been previously used in internal control research,offering new opportunities for exploring the relationship between employee feedback and control weaknesses,and shedding light on potential improvements in internal control practices.Downstream stakeholders(such as researchers,management,in-vestors,and auditors)can benefit by having rapid,automated means for filtering and prioritizing employee reviews for further investigation,with respect to accounting control issue mentions.
基金supported by the National Natural Science Foundation of China(NSFC)(Grant No.72171095)the National Social Science Foundation of China(Grant No.22VRC153)the Wuhan Textile University Fund(Grant Nos.2024289 and 2024380)。
文摘Although generative conversational artificial intelligence(AI)can answer questions well and hold conversations as a person,the semantic ambiguity inherent in text-based communication poses challenges to effective use.Effective use reflects the users’utilization of generative conversational AI to achieve their goals,which has not been previously studied.Drawing on the media naturalness theory,we examined how generative conversational AI’s content and style naturalness affect effective use.A two-wave survey was conducted to collect data from 565 users of generative conversational AI.Two techniques were used in this study.Initially,partial least squares structural equation modeling(PLS-SEM)was applied to determine the variables that significantly affected the mechanisms(i.e.,cognitive effort and communication ambiguity)and effective use.Secondly,an artificial neural network model was used to evaluate the relative importance of the significant predictors of mechanisms and effective use identified from the PLS-SEM analysis.The results revealed that the naturalness of content and style differed in their effects on cognitive effort and communication ambiguity.Additionally,cognitive effort and communication ambiguity negatively affected effective use.This study advances the literature on effective use by uncovering the psychological mechanisms underlying effective use and their antecedents.In addition,this study offers insights into the design of generative conversational AI.
