Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.

Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems

Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.

Evaluating and Addressing Interoperability Constraints in Health Information Systems for Enhanced Healthcare Delivery in Zimbabwe

Interoperability constraints in health information systems pose significant challenges to the seamless exchange and utilization of health data, hindering effective healthcare delivery. This paper aims to evaluate and address these constraints to enhance healthcare delivery. The study examines the current state of interoperability in health information systems, identifies the key constraints, and explores their impact on healthcare outcomes. Various approaches and strategies for addressing interoperability constraints are discussed, including the adoption of standardized data formats, implementation of interoperability frameworks, and establishment of robust data governance mechanisms. Furthermore, the study highlights the importance of stakeholder collaboration, policy development, and technical advancements in achieving enhanced interoperability. The findings emphasize the need for a comprehensive evaluation of interoperability constraints and the implementation of targeted interventions to promote seamless data exchange, improve care coordination, and enhance patient outcomes in healthcare settings.

Foundations and Applications of Information Systems Dynamics

Although numerous advances have been made in information technology in the past decades,there is still a lack of progress in information systems dynamics(ISD),owing to the lack of a mathematical foundation needed to describe information and the lack of an analytical framework to evaluate information systems.The value of ISD lies in its ability to guide the design,development,application,and evaluation of largescale information system-of-systems(So Ss),just as mechanical dynamics theories guide mechanical systems engineering.This paper reports on a breakthrough in these fundamental challenges by proposing a framework for information space,improving a mathematical theory for information measurement,and proposing a dynamic configuration model for information systems.In this way,it establishes a basic theoretical framework for ISD.The proposed theoretical methodologies have been successfully applied and verified in the Smart Court So Ss Engineering Project of China and have achieved significant improvements in the quality and efficiency of Chinese court informatization.The proposed ISD provides an innovative paradigm for the analysis,design,development,and evaluation of large-scale complex information systems,such as electronic government and smart cities.

Applying the Technology Acceptance Model (TAM) in Information Technology System to Evaluate the Adoption of Decision Support System

With the beginning of the information systems’ spreading, people started thinking about using them for making business decisions. Computer technology solutions, such as the Decision Support System, make the decision-making process less complex and simpler for problem-solving. In order to make a high-quality business decision, managers need to have a great deal of appropriate information. Nonetheless, this complicates the process of making appropriate decisions. In a situation like that, the possibility of using DSS is quite logical. The aim of this paper is to find out the intended use of DSS for medium and large business organizations in USA by applying the Technology Acceptance Model (TAM). Different models were developed in order to understand and predict the use of information systems, but the information systems community mostly used TAM to ensure this issue. The purpose of the research model is to determine the elements of analysis that contribute to these results. The sample for the research consisted of the target group that was supposed to have completed an online questionnaire about the manager’s use of DSS in medium and large American companies. The information obtained from the questionnaires was analyzed through the SPSS statistical software. The research has indicated that, this is primarily used due to a significant level of Perceived usefulness and For the Perceived ease of use.

An Optimized Method for Accounting Information in Logistic Systems

In the era of rapid information development,with the popularity of computers,the advancement of science and technology,and the ongoing expansion of IT technology and business,the enterprise resource planning(ERP)system has evolved into a platform and a guarantee for the fulfilment of company management procedures after long-term operations.Because of developments in information technology,most manual accounting procedures are being replaced by computerized Accounting Information Systems(AIS),which are quicker and more accurate.The primary factors influencing the decisions of logistics firm trading parties are investigated in order to enhance the design of decision-supporting modules and to improve the performance of logistics enterprises through AIS.This paper proposed a novel approach to calculate the weights of each information element in order to establish their important degree.The main purpose of this research is to present a quantitative analytic approach for determining the important information of logistics business collaboration response.Furthermore,the idea of total orders and the significant degrees stated above are used to identify the optimal order of all information elements.Using the three ways of marginal revenue,marginal cost,and business matching degree,the information with cumulative weights is which is deployed to form the data from the intersection of the best order.It has the ability to drastically reduce the time and effort required to create a logistics business control/decision-making system.

ISO25000-Related Metrics for Evaluating the Quality of Complex Information Systems

Evaluating complex information systems necessitates deep contextual knowledge of technology, user needs, and quality. The quality evaluation challenges increase with the system’s complexity, especially when multiple services supported by varied technological modules, are offered. Existing standards for software quality, such as the ISO25000 series, provide a broad framework for evaluation. Broadness offers initial implementation ease albeit, it often lacks specificity to cater to individual system modules. This paper maps 48 data metrics and 175 software metrics on specific system modules while aligning them with ISO standard quality traits. Using the ISO25000 series as a foundation, especially ISO25010 and 25012, this research seeks to augment the applicability of these standards to multi-faceted systems, exemplified by five distinct software modules prevalent in modern information ecosystems.

Cybersecurity Requirements for Management Information Systems

Cybersecurity is therefore one of the most important elements of security in developed countries. Especially since there is an overall trend towards cybersecurity in all aspects of life, I have found that the idea of cybersecurity is based on protecting critical facilities: The nation’s information infrastructure. Information systems, including e-government management systems, are managed by key state agencies. As with economic, scientific, commercial, and other systems, threats are threats to a nation’s national security. We have therefore found that many countries are preparing institutions capable of integrating cybersecurity into protection, development, and information security. This concept has become the most important concern of developed countries, which have secured all scientific possibilities and systems to achieve it. The electronic information network has become an integral part of today’s daily lives in all places. In addition to personal uses, digital information is used, processed, stored, and shared. As this information increases and spreads, we have found that its protection has become more vital and has an effective impact on national security and technical progress.

A Novel High-Efficiency Transaction Verification Scheme for Blockchain Systems

Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the system.This technology has been widely used and has developed rapidly in big data systems across various fields.An increasing number of users are participating in application systems that use blockchain as their underlying architecture.As the number of transactions and the capital involved in blockchain grow,ensuring information security becomes imperative.Addressing the verification of transactional information security and privacy has emerged as a critical challenge.Blockchain-based verification methods can effectively eliminate the need for centralized third-party organizations.However,the efficiency of nodes in storing and verifying blockchain data faces unprecedented challenges.To address this issue,this paper introduces an efficient verification scheme for transaction security.Initially,it presents a node evaluation module to estimate the activity level of user nodes participating in transactions,accompanied by a probabilistic analysis for all transactions.Subsequently,this paper optimizes the conventional transaction organization form,introduces a heterogeneous Merkle tree storage structure,and designs algorithms for constructing these heterogeneous trees.Theoretical analyses and simulation experiments conclusively demonstrate the superior performance of this scheme.When verifying the same number of transactions,the heterogeneous Merkle tree transmits less data and is more efficient than traditional methods.The findings indicate that the heterogeneous Merkle tree structure is suitable for various blockchain applications,including the Internet of Things.This scheme can markedly enhance the efficiency of information verification and bolster the security of distributed systems.

《Information Systems Research》1998~2017文献计量分析—基于Citespace可视图谱

本文运用Citespace文献计量分析软件,以《Information Systems Research》(简称ISR)期刊在1998年至2017年近20年收录的747篇文献为研究对象,从文献产出分析、国家合作网络分析、机构合作网络分析和引文网络聚类分析四个方面进行研究,研究结果表明:1) 根据文献产出分析,ISR期刊的文献产出量呈现波动性增长;2) 根据国家合作网络分析,美国在国家合作网络中是核心节点,中国需要加强国际合作,提高自身影响力;3) 在研究机构合作网络中,文献产出量前五所高校均位于美国,同时根据合作网络中心性分析,中心性在0.1以上的12所机构中,9所位于美国,中国只有香港城市大学列于其中,另外,德克萨斯大学达拉斯分校、佐治亚理工学院、新加坡国立大学和德克萨斯大学奥斯汀分校,在信息系统领域研究潜力巨大;4) 根据引文网络聚类分析,研究热点的演变可分为三个阶段:1998年至2001年期间信息系统领域研究的重心在信息系统的企业应用,2002年至2011年期间研究重心由传统的组织转移到虚拟组织的测量指标、信任和影响因素,2012年至2017年期间由电子口碑研究转向在线知识社区中的知识扩散研究。

Approximation Maintenance When Adding a Conditional Value in Set-Valued Ordered Decision Systems

The integration of set-valued ordered rough set models and incremental learning signify a progressive advancement of conventional rough set theory, with the objective of tackling the heterogeneity and ongoing transformations in information systems. In set-valued ordered decision systems, when changes occur in the attribute value domain, such as adding conditional values, it may result in changes in the preference relation between objects, indirectly leading to changes in approximations. In this paper, we effectively addressed the issue of updating approximations that arose from adding conditional values in set-valued ordered decision systems. Firstly, we classified the research objects into two categories: objects with changes in conditional values and objects without changes, and then conducted theoretical studies on updating approximations for these two categories, presenting approximation update theories for adding conditional values. Subsequently, we presented incremental algorithms corresponding to approximation update theories. We demonstrated the feasibility of the proposed incremental update method with numerical examples and showed that our incremental algorithm outperformed the static algorithm. Ultimately, by comparing experimental results on different datasets, it is evident that the incremental algorithm efficiently reduced processing time. In conclusion, this study offered a promising strategy to address the challenges of set-valued ordered decision systems in dynamic environments.

Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

社会化网络服务世界中的有效竞争策略——Information Systems Research, Vol. 21, No. 3, September 2010综述

一个国家的企业与其他国家的企业在本土市场上开展的正面直接竞争过程,既是技术创新与市场营销有效结合并赢得顾客的过程,也是技术创新的竞争过程。面对日益激烈的竞争环境,尤其是高科技企业不仅需要平衡技术和市场策略的投入。

An Optimized Method for Information System Transactions Based on Blockchain

Accounting Information System(AIS),which is the foundation of any enterprise resource planning(ERP)system,is often built as centralized system.The technologies that allow the Internet-of-Value,which is built onfive aspects that are network,algorithms,distributed ledger,transfers,and assets,are based on blockchain.Cryptography and consensus protocols boost the blockchain plat-form implementation,acting as a deterrent to cyber-attacks and hacks.Blockchain platforms foster innovation among supply chain participants,resulting in ecosys-tem development.Traditional business processes have been severely disrupted by blockchains since apps and transactions that previously required centralized struc-tures or trusted third-parties to authenticate them may now function in a decentra-lized manner with the same level of assurance.Because a blockchain split in AIS may easily lead to double-spending attacks,reducing the likelihood of a split has become a very important and difficult research subject.Reduced block relay time between the nodes can minimize the block propagation time of all nodes,resulting in better Bitcoin performance.In this paper,three problems were addressed on transaction and block propagation mechanisms in order to reduce the likelihood of a split.A novel algorithm for blockchain is proposed to reduce the total pro-pagation delay in AIS transactions.Numerical results reveal that,the proposed algorithm performs better and reduce the transaction delay in AIS as compared with existing methods.

Analysis of Secured Cloud Data Storage Model for Information

This paper was motivated by the existing problems of Cloud Data storage in Imo State University, Nigeria such as outsourced data causing the loss of data and misuse of customer information by unauthorized users or hackers, thereby making customer/client data visible and unprotected. Also, this led to enormous risk of the clients/customers due to defective equipment, bugs, faulty servers, and specious actions. The aim if this paper therefore is to analyze a secure model using Unicode Transformation Format (UTF) base 64 algorithms for storage of data in cloud securely. The methodology used was Object Orientated Hypermedia Analysis and Design Methodology (OOHADM) was adopted. Python was used to develop the security model;the role-based access control (RBAC) and multi-factor authentication (MFA) to enhance security Algorithm were integrated into the Information System developed with HTML 5, JavaScript, Cascading Style Sheet (CSS) version 3 and PHP7. This paper also discussed some of the following concepts;Development of Computing in Cloud, Characteristics of computing, Cloud deployment Model, Cloud Service Models, etc. The results showed that the proposed enhanced security model for information systems of cooperate platform handled multiple authorization and authentication menace, that only one login page will direct all login requests of the different modules to one Single Sign On Server (SSOS). This will in turn redirect users to their requested resources/module when authenticated, leveraging on the Geo-location integration for physical location validation. The emergence of this newly developed system will solve the shortcomings of the existing systems and reduce time and resources incurred while using the existing system.

Research on fault-tolerant control of networked control systems based on information scheduling

A kind of networked control system is studied; the networked control system with noise disturbance is modeled based on information scheduling and control co-design. Augmented state matrix analysis method is introduced, and robust fault-tolerant control problem of networked control systems with noise disturbance under actuator failures is studied. The parametric expression of the controller under actuator failures is given. Furthermore, the result is analyzed by simulation tests, which not only satisfies the networked control systems stability, but also decreases the data information number in network channel and makes full use of the network resources.

Remote sensing and geographic information systems techniques in studies on treeline ecotone dynamics

We performed a meta-analysis on over 100 studies applying remote sensing(RS)and geographic information systems(GIS)to understand treeline dynamics.A literature search was performed in multiple online databases,including Web of Knowledge(Thomson Reuters),Scopus(Elsevier),BASE(Bielefeld Academic Search Engine),CAB Direct,and Google Scholar using treeline-related queries.We found that RS and GIS use has steadily increased in treeline studies since 2000.Spatialresolution RS and satellite imaging techniques varied from low-resolution MODIS,moderate-resolution Landsat,to high-resolution WorldView and aerial orthophotos.Most papers published in the 1990s used low to moderate resolution sensors such as Landsat Multispectral Scanner and Thematic Mapper,or SPOT PAN(Panchromatic)and MX(Multispectral)RS images.Subsequently,we observed a rise in high-resolution satellite sensors such as ALOS,GeoEye,IKONOS,and WorldView for mapping current and potential treelines.Furthermore,we noticed a shift in emphasis of treeline studies over time:earlier reports focused on mapping treeline positions,whereas RS and GIS are now used to determine the factors that control treeline variation.

Attributes reduct and decision rules optimization based on maximal tolerance classification in incomplete information systems with fuzzy decisions

A new approach to knowledge acquisition in incomplete information system with fuzzy decisions is proposed. In such incomplete information system, the universe of discourse is classified by the maximal tolerance classes, and fuzzy approximations are defined based on them. Three types of relative reducts of maximal tolerance classes are then proposed, and three types of fuzzy decision rules based on the proposed attribute description are defined. The judgment theorems and approximation discernibility functions with respect to them are presented to compute the relative reduct by using Boolean reasoning techniques, from which we can derive optimal fuzzy decision rules from the systems. At last, three types of relative reducts of the system and their computing methods are given.

The Approach to Probabilistic Decision-Theoretic Rough Set in Intuitionistic Fuzzy Information Systems

For the moment, the representative and hot research is decision-theoretic rough set (DTRS) which provides a new viewpoint to deal with decision-making problems under risk and uncertainty, and has been applied in many fields. Based on rough set theory, Yao proposed the three-way decision theory which is a prolongation of the classical two-way decision approach. This paper investigates the probabilistic DTRS in the framework of intuitionistic fuzzy information system (IFIS). Firstly, based on IFIS, this paper constructs fuzzy approximate spaces and intuitionistic fuzzy (IF) approximate spaces by defining fuzzy equivalence relation and IF equivalence relation, respectively. And the fuzzy probabilistic spaces and IF probabilistic spaces are based on fuzzy approximate spaces and IF approximate spaces, respectively. Thus, the fuzzy probabilistic approximate spaces and the IF probabilistic approximate spaces are constructed, respectively. Then, based on the three-way decision theory, this paper structures DTRS approach model on fuzzy probabilistic approximate spaces and IF probabilistic approximate spaces, respectively. So, the fuzzy decision-theoretic rough set (FDTRS) model and the intuitionistic fuzzy decision-theoretic rough set (IFDTRS) model are constructed on fuzzy probabilistic approximate spaces and IF probabilistic approximate spaces, respectively. Finally, based on the above DTRS model, some illustrative examples about the risk investment of projects are introduced to make decision analysis. Furthermore, the effectiveness of this method is verified.

Non-intrusive hybrid interval method for uncertain nonlinear systems using derivative information

This paper proposes a new non-intrusive hybrid interval method using derivative information for the dynamic response analysis of nonlinear systems with uncertain-but- bounded parameters and/or initial conditions. This method provides tighter solution ranges compared to the existing polynomial approximation interval methods. Interval arith- metic using the Chebyshev basis and interval arithmetic using the general form modified affine basis for polynomials are developed to obtain tighter bounds for interval computation. To further reduce the overestimation caused by the ＂wrap- ping effect＂ of interval arithmetic, the derivative information of dynamic responses is used to achieve exact solutions when the dynamic responses are monotonic with respect to all the uncertain variables. Finally, two typical numerical examples with nonlinearity are applied to demonstrate the effective- ness of the proposed hybrid interval method, in particular, its ability to effectively control the overestimation for specific timepoints.