Immune algorithm for discretization of decision systems in rough set theory

Rough set theory plays an important role in knowledge discovery, but cannot deal with continuous attributes, thus discretization is a problem which we cannot neglect. And discretization of decision systems in rough set theory has some particular characteristics. Consistency must be satisfied and cuts for discretization is expected to be as small as possible. Consistent and minimal discretization problem is NP-complete. In this paper, an immune algorithm for the problem is proposed. The correctness and effectiveness were shown in experiments. The discretization method presented in this paper can also be used as a data pre- treating step for other symbolic knowledge discovery or machine learning methods other than rough set theory.

Fast assignment reduction in inconsistent incomplete decision systems

This paper focuses on fast algorithm for computing the assignment reduct in inconsistent incomplete decision systems. It is quite inconvenient to judge the assignment reduct directly ac-cording to its definition. We propose the judgment theorem for the assignment reduct in the inconsistent incomplete decision system, which greatly simplifies judging this type reduct. On such basis, we derive a novel attribute significance measure and construct the fast assignment reduction algorithm （F-ARA）, intended for com-puting the assignment reduct in inconsistent incomplete decision systems. Final y, we make a comparison between F-ARA and the discernibility matrix-based method by experiments on 13 Univer-sity of California at Irvine （UCI） datasets, and the experimental results prove that F-ARA is efficient and feasible.

An Information Analysis Decision System

In recent years, we are increasingly coping with macro, complex and large-scale system in whichinformations twist together. It is a worthwhile research to develop general computer-aided meth-od for decision-making on the basis of processing, analysing and deducing from the informationobtained.

Effective Approach to Elevate the Intelligence of Management Decision System

Based on the sticking point of the low intelligence of the existing management decision system,this paper puts forward the idea of enriching and refining the knowledge of the system and endowing it with the ability to learn by means of adopting three types of heterogeneous knowledge representation and knowledge management measures.At length,this paper outlines the basic framework of an intelligence system for the sake of management decision problem.

Research into the Decision-maker's Utility in the Enterprise's Decision System Based on Network Economy Environment

The decision system based on network economy is the foundation of enterprise's making good winning in its market. This paper describes the decision makers' utility model based on network economy, considers the roles decision-makers not only play in the enterprises are decision making, coordinating, controlling and monitoring, but also they are mainly designers, executants and educators in the mode of network economy

Attribute Reduction in Decision Systems Based on Relation Matrix

This paper proposes,from the viewpoint of relation matrix,a new algorithm of attribute reduction for decision systems.Two new and relative reasonable indices are first defined to measure significance of the attributes in decision systems and then a heuristic algorithm of attribute reduction is formulated.Moreover,the time complexity of the algorithm is analyzed and it is proved to be complete.Some numerical experiments are also conducted to access the performance of the presented algorithm and the results demonstrate that it is not only effective but also efficient.

Research on Stealth Assistant Decision System of Submarine Voyage Stage

Stealth security has always been considered as an important guarantee for the vitality and combat effectiveness of submarines.In accordance with the stealth requirements of submarines performing stealth voyage tasks,this paper proposes a stealth assistant decision system.Firstly,the submarine stealth posture is acquired.A fuzzy neural network inference engine based on improved simplified particle swarm optimization is designed.The auxiliary decision-making scheme for state control and maneuver avoidance of submarine and its equipment is automatically generated.Secondly,the simulation and deduction of the assistant decision-making scheme are realized by the calculation modules of sound source level,propagation loss,and stealth situation.The assistant decision-making scheme and simulation result provide decision support for the commander.Thirdly,the simulation experiment platform of the submarine stealth assistant decision system is constructed.The submarine stealth assistant decision system described in this paper can quickly and efficiently produce assistant decision-making schemes,including submarine and equipment control and maneuver avoidance.The scheme is in line with the combat experience and the results of the pre-model simulation experiments,whereas the simulation deduction evaluates the rationality and effectiveness of the selected scheme.The submarine stealth assistant decision system can adapt to a complex battlefield environment in addition to rapidly and accurately providing assistance in decision-making.

Research on City Rapid Medical Decision System based on GIS

In order to guarantee the validity of the city emergency rescue, it will need to feedback goods demand and injuries to guide accident emergency rescue center in a timely, and also we need to fully grasp with emergency supplies and emergency hospital. Considering the effectiveness of city emergency rescue and timeliness, the paper analyzes and design Rescue Decision Support System for city distribution of emergency supplies and search of wounded man based on GIS. Emergency medical aid model established in this paper, taking ArcGIS＋VS2005 as the development platform, C# as development language, design and development the preliminary city emergency medical rescue simulation system based on GIS.

Research on Energy Decision System based on Network Technology

This paper analyzes the C/S structure and B/S structure of their own characteristics, building energy decision support system based on network technology, elaborated Web GIS and database connections and other technical principles and applications. Mixed B/S structure mode development to achieve information management, energy consumption is forecast to show the query using the amomat of energy, energy supply and demand dynamic equilibrimn analysis and other functions, with simple, efficient, easy to operate and so on, and has good scalability and maintainability.

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.

Optimizing the Clinical Decision Support System (CDSS) by Using Recurrent Neural Network (RNN) Language Models for Real-Time Medical Query Processing

This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning techniques.Specifically,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state cells.These models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal diseases.Traditional diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed decisions.Our goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic support.We propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies effectively.Additionally,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN models.We further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and imbalance.Comprehensive validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’reliability.Moreover,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model interpretability.Our findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.

Design and Implementation of the Employment Management Decision Support System based on Machine Learning

To address the challenges of current college student employment management,this study designed and implemented a machine learning-based decision support system for college student employment management.The system collects and analyzes multidimensional data,uses machine learning algorithms for prediction and matching,provides personalized employment guidance for students,and provides decision support for universities and enterprises.The research results indicate that the system can effectively improve the efficiency and accuracy of employment guidance,promote school-enterprise cooperation,and achieve a win-win situation for all parties.

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.

Method of Establishing Object-Oriented System Structure for Decision Support System

In order to solve existing problems about the method of establishing traditional system structure of decision support system(DSS), O S chart is applied to describe object oriented system structure of general DSS, and a new method of eight specific steps is proposed to establish object oriented system structure of DSS by using the method of O S chart, which is applied successfully to the development of the DSS for the energy system ecology engineering research of the Wangheqiu country. Supplying many scientific effective computing models, decision support ways and a lot of accurate reliable decision data, the DSS plays a critical part in helping engineering researchers to make correct decisions. Because the period for developing the DSS is relatively shorter, the new way improves the efficiency of establishing DSS greatly. It also makes the DSS of system structure more flexible and easy to expand.

Dominance-based fuzzy rough approach to an interval-valued decision system

Though the dominance-based rough set approach has been applied to interval-valued information systems for knowledge discovery, the traditional dominance relation cannot be used to describe the degree of dominance principle in terms of pairs of objects. In this paper, a ranking method of interval-valued data is used to describe the degree of dominance in the interval-valued information system. Therefore, the fuzzy rough technique is employed to construct the rough approximations of upward and downward unions of decision classes, from which one can induce at least and at most decision rules with certainty factors from the interval-valued decision system. Some numerical examples are employed to substantiate the conceptual arguments.

Research of Decision System Analysis for Decision Support

In the Paper.the decision system,its classification,and its charchterizing description have been discussed.We Proposed a method of problem recognition,a method of problem description and a method of problem solving etc.It is pointed out that the decision system can be classified into two kinds:open-loop decision system,and close-loop decision system,the difference between them are that there are functions of recognizing problem and tracking results in close-loop decision system.We believe that,he close-looP decision system should be researched into more deeply to enhance the effectiveness of decisionmaking and decision support.

Research and Application of Maize Precision Intelligence Spatial Decision Support System

In order to solve the problem of the maze precision fertilizer,soil fertility evaluation,soil fertility classify and yield projections,the geographic information system with spatial information processing functions,spatial data mining techniques with spatial information analysis capabilities,expert system technology in the field of artificial intelligence,traditional information management systems and decision support system were effectively integrated in this study,and the statistical analysis method of GIS and data visualization were combined to design and implement the maize precise intelligent space decision-making system.This system had greatly improved the decision-making ability in agricultural production carried out by agricultural management.

Study on the Decision Support System for Northing of Winter Wheat Cultivation in Hebei Province

[Objective] This study was to provide methods to improve the scientificity and informatization level of agricultural decision-making system based on the study of Decision Support System for ＂Northing of Winter Wheat＂ in Hebei Province （DSS- NWWH）. [Method] The functions, development process, operation guidance as well as input and output modes of DSSNWWH were introduced, and the simulated results of the system were verified by comparing with the actual situations. [Result] The decision support system established in this study could predict whether a wheat variety could live through the winter in a certain area of northern Hebei Province, as well as the growth conditions based on the previous meteorological data or local weather forecast, and provided corresponding cultivation and management measures, making it possible for the user to determine whether the variety could be planted in the region based on the predictions. [Conclusion] The established DSSNWWH in this study can effectively help decision makers make decisions, providing scientific instructions for the northing of winter wheat.

A Decision Support System for Urban Landscape Planning Based on GIS

Based on platform of GIS software ArcView and theory of management information system(MIS), a decision support system on urban landscape planning was designed via GIS technology, module design technique and object-oriented programming technique. The function of this system is realized by its two subsystems—one is for height limit model of city and another is for landscape belt planning, which can help administors in landscape planning.

Research on coal mine safety production management decision system based on VB

Based on safety management appraisal theory, the decision system was divided into 5 function menu module, including system control module, mining coal and the tunneling working surface security evaluation module, the entire ore safety production condition appraisal module, the safety management level appraisal module of main production work area, the withdrawal system module and so on. The system operates through the constitutive procedure, outputs the main operation results by graph and form, and realizes the main function of safety evaluation.