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.

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.

Assessing Suitability of Irrigation Scheduling Decision Support Systems for Lowland Rice Farmers in Sub-Saharan Africa—A Review

Irrigation in lowland rice production systems in Sub-Saharan Africa (SSA) is mainly based on traditional surface irrigation methods with continuous flooding practices. This irrigation method ends up using a lot more water that would have otherwise been used to open more land and be used in other water-requiring sectors. Various studies suggest Alternate Wetting and Drying (AWD) as an alternative practice for water management that reduces water use without significantly affecting yield. However, this practice has not been well adopted by the farmers despite its significant benefits of reduced total water use. Improving the adoption of AWD using irrigation Decision Support Systems (DSSs) helps the farmer on two fronts;to know “how much water to apply” and “when to irrigate”, which is very critical in maximizing productivity. This paper reviews the applicability of DSSs using AWD in lowland rice production systems in Sub-Saharan Africa.

Optimizing Vaccine Access: A Web-Based Scheduling System with Geo-Tagging Integration and Decision Support for Local Health Centers

The system created aims to produce an online vaccination appointment scheduling system with geo-tagging integration and a decision-support mechanism for neighborhood health clinics. With a decision support mechanism that suggests the essential vaccines based on their account details, it is made to meet the unique vaccination needs of each patient. The system includes immunizations that are accessible locally, and patients and midwives can manage their own corresponding information through personal accounts. Viewers of websites can visualize the distribution of vaccines by purok thanks to geotagging. The Agile Scrum Methodology was modified by the researchers for early delivery, change flexibility, and continual system improvement in order to accomplish the study’s main goal. In order to assess the system’s acceptability in terms of functional adequacy, performance efficiency, compatibility, usability, reliability, security, maintainability, and portability, it was designed in accordance with the ISO 25010 Product Software Quality Standards. Following the assessment, the system was given an average total weighted mean score of 4.62, which represents a verbal interpretation of “strongly agree”. This score demonstrates that the evaluators were in agreement that the system met the requirements of ISO 25010 for Product Software Quality Standards.

Progress of clinical decision support systems in stroke nursing care

Stroke is characterized by high incidence,high recurrence,high disability,and high morbidity and mortality in China,resulting in a heavy social and clinical burden.A clinical decision support system,as an intelli-gent computer system,can assist nurses in decision-mak-ing to collect information quickly,make the most suitable personalized decisions for patients,and improve nurses’decision-making judgment and quality of care.Promoting the development and application of decision support sys-tems in stroke nursing significantly enhances the nursing staff’s work quality and patients’prognosis.Therefore,this paper reviews the research progress of domestic and international clinical decision support systems in stroke nursing care to provide other researchers with specific research directions for developing and applying decision support systems in stroke nursing care.

A behavior and physiology-based decision support tool to predict thermal comfort and stress in non-pregnant,mid-gestation,and late-gestation sows

Background:Although thermal indices have been proposed for swine,none to our knowledge differentiate by reproductive stage or predict thermal comfort using behavioral and physiological data.The study objective was to develop a behavior and physiology-based decision support tool to predict thermal comfort and stress in multiparous(3.28±0.81)non-pregnant(n=11),mid-gestation(n=13),and late-gestation(n=12)sows.Results:Regression analyses were performed using PROC MIXED in SAS 9.4 to determine the optimal environmental indicator[dry bulb temperature(TDB)and dew point]of heat stress(HS)in non-pregnant,mid-gestation,and lategestation sows with respiration rate(RR)and body temperature(TB)successively used as the dependent variable in a cubic function.A linear relationship was observed for skin temperature(T_(S))indicating that TDB rather than the sow HS response impacted T_(S)and so T_(S)was excluded from further analyses.Reproductive stage was significant for all analyses(P<0.05).Heat stress thresholds for each reproductive stage were calculated using the inflections points of RR for mild HS and TB for moderate and severe HS.Mild HS inflection points differed for non-pregnant,mid-gestation,and late gestation sows and occurred at 25.5,25.1,and 24.0℃,respectively.Moderate HS inflection points differed for non-pregnant,mid-gestation,and late gestation sows and occurred at 28.1,27.8,and 25.5℃,respectively.Severe HS inflection points were similar for non-pregnant and mid-gestation sows(32.9℃)but differed for late-gestation sows(30.8℃).These data were integrated with previously collected behavioral thermal preference data to estimate the TDB that non-pregnant,mid-gestation,and late-gestation sows found to be cool(TDB<TDB preference range),comfortable(TDB=TDB preference range),and warm(TDB preference range<TDB<mild HS).Conclusions:The results of this study provide valuable information about thermal comfort and thermal stress thresholds in sows at three reproductive stages.The development of a behavior and physiology-based decision support tool to predict thermal comfort and stress in non-pregnant,mid-gestation,and late-gestation sows is expected to provide swine producers with a more accurate means of managing sow environments.

Artificial Intelligence Enabled Decision Support System on E-Healthcare Environment

In today’s digital era,e-healthcare systems exploit digital technologies and telecommunication devices such as mobile devices,computers and the inter-net to provide high-quality healthcare services.E-healthcare decision support sys-tems have been developed to optimize the healthcare services and enhance a patient’s health.These systems enable rapid access to the specialized healthcare services via reliable information,retrieved from the cases or the patient histories.This phenomenon reduces the time taken by the patients to physically visit the healthcare institutions.In the current research work,a new Shuffled Frog Leap Optimizer with Deep Learning-based Decision Support System(SFLODL-DSS)is designed for the diagnosis of the Cardiovascular Diseases(CVD).The aim of the proposed model is to identify and classify the cardiovascular diseases.The proposed SFLODL-DSS technique primarily incorporates the SFLO-based Feature Selection(SFLO-FS)approach for feature subset election.For the pur-pose of classification,the Autoencoder with Gated Recurrent Unit(AEGRU)model is exploited.Finally,the Bacterial Foraging Optimization(BFO)algorithm is employed tofine-tune the hyperparameters involved in the AEGRU method.To demonstrate the enhanced performance of the proposed SFLODL-DSS technique,a series of simulations was conducted.The simulation outcomes established the superiority of the proposed SFLODL-DSS technique as it achieved the highest accuracy of 98.36%.Thus,the proposed SFLODL-DSS technique can be exploited as a proficient tool in the future for the detection and classification of CVD.

An Intelligent Decision Support System for Lung Cancer Diagnosis

Lung cancer is the leading cause of cancer-related death around the globe.The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis.Most diagnostic techniques can identify and classify only one type of lung cancer.It is crucial to close this gap with a system that detects all lung cancer types.This paper proposes an intelligent decision support system for this purpose.This system aims to support the quick and early detection and classification of all lung cancer types and subtypes to improve treatment and save lives.Its algorithm uses a Convolutional Neural Network(CNN)tool to perform deep learning and a Random Forest Algorithm(RFA)to help classify the type of cancer present using several extracted features,including histograms and energy.Numerous simulation experiments were conducted on MATLAB,evidencing that this system achieves 98.7%accuracy and over 98%precision and recall.A comparative assessment assessing accuracy,recall,precision,specificity,and F-score between the proposed algorithm and works from the literature shows that the proposed system in this study outperforms existing methods in all considered metrics.This study found that using CNNs and RFAs is highly effective in detecting lung cancer,given the high accuracy,precision,and recall results.These results lead us to believe that bringing this kind of technology to doctors diagnosing lung cancer is critical.

Multi-agent decision support system for missile defense based on improved PSO algorithm

Ballistic missile defense system (BMDS) is important for its special role in ensuring national security and maintaining strategic balance. Research on modeling and simulation of the BMDS beforehand is essential as developing a real one requires lots of manpower and resources. BMDS is a typical complex system for its nonlinear, adaptive and uncertainty characteristics. The agent-based modeling method is well suited for the complex system whose overall behaviors are determined by interactions among individual elements. A multi-agent decision support system (DSS), which includes missile agent, radar agent and command center agent, is established based on the studies of structure and function of BMDS. Considering the constraints brought by radar, intercept missile, offensive missile and commander, the objective function of DSS is established. In order to dynamically generate the optimal interception plan, the variable neighborhood negative selection particle swarm optimization (VNNSPSO) algorithm is proposed to support the decision making of DSS. The proposed algorithm is compared with the standard PSO, constriction factor PSO (CFPSO), inertia weight linear decrease PSO (LDPSO), variable neighborhood PSO (VNPSO) algorithm from the aspects of convergence rate, iteration number, average fitness value and standard deviation. The simulation results verify the efficiency of the proposed algorithm. The multi-agent DSS is developed through the Repast simulation platform and the constructed DSS can generate intercept plans automatically and support three-dimensional dynamic display of missile defense process.

Development of a GIS-based Decision Support System for Assessing Land Use Status

The objectives of this study are to assess land s ui tability and to predict the spatial and temporal changes in land use types (LUTs ) by using GIS-based land use management decision support system. A GIS databas e with data on climate, topography, soil characteristic, irrigation condition, f ertilizer application, and special socioeconomic activities has been developed a nd used for the evaluation of land productivity for different crops by integrati ng with a crop growth model-the erosion productivity impact calculator (EPIC). International food policy simulation model (IFPSIM) is also embedded into GIS fo r the predictions of how crop demands and crop market prices will change under a lternative policy scenarios. An inference engine (IE) including land use choice model is developed to illustrate land use choice behavior based on logit models , which allows to analyze how diversified factors ranging from climate changes, crop price changes to land management changes can affect the distribution of agr icultural land use. A test for integrated simulation is taken in each 0.1° by 0.1° grid cell to predict the change of agricultural land use types at globa l level. Global land use changes are simulated from 1992 to 2050.

Review of state-of-the-art decision support systems (DSSs) for prevention and suppression of forest fires

Forest ecosystems are our priceless natural resource and are a key component of the global carbon budget. Forest fires can be a hazard to the viability and sustainable management of forests with consequences for natural and cultural environments, economies, and the life quality of local and regional populations. Thus, the selection of strategies to manage forest fires, while considering both functional and economic efficiency, is of primary importance. The use of decision support systems(DSSs) by managers of forest fires has rapidly increased. This has strengthened capacity to prevent and suppress forest fires while protecting human lives and property. DSSs are a tool that can benefit incident management and decision making and policy, especially for emergencies such as natural disasters. In this study we reviewed state-of-the-art DSSs that use: database management systems and mathematical/economic algorithms for spatial optimization of firefighting forces; forest fire simulators and satellite technology for immediate detection and prediction of evolution of forest fires; GIS platforms that incorporate several tools to manipulate, process and analyze geographic data and develop strategic and operational plans.

AN INTELLIGENT DECISION SUPPORT SYSTEM （IDSS） IN THE OPERATION PROCESS OF ELECTRIC FURNACE FOR CLEANING SLAG

In this paper, the structure and function of the IDSS in the operation process of electric furnace for cleaning slag are presented and the fuzzy neural network decision model (FNNDM) in the IDSS is specially suggested. The IDSS possesses selflearning and adaptive properties, and has been used for managing and analyzing the optimal operational conditions since June 1992. Electric energy consumption has been reduced remarkably and the coefficient of recovery of cobalt and nickel has been increased.

ANINTELLIGENT DECISION SUPPORTSYSTEM（IDSS） ONTHEPROCESSOFNICKELMATTESMELTER

This paper describes the structure and function of the intelligent decision support system (IDSS) on the process of nickel matte smelter. The knowledge and model base system based on fuzzy-decision rules ale specially suggested. The IDSS possesses the self-learning and adaptive properties, andhas been used for managing and analyzing the production information, optimizing the composition of the charge mixture, and deciding the optimal operational conditions. Electric energy consumption has been reduced remarkably and the yield of nickel has been increased.

Model based decision support system for land use changes and socio-economic assessments

Hydrological models are often linked with other models in cognate sciences to understand the interactions among climate, earth, water, ecosystem, and human society. This paper presents the development and implementation of a decision support system（DSS） that links the outputs of hydrological models with real-time decision making on social-economic assessments and land use management. Discharge and glacier geometry changes were simulated with hydrological model, water availability in semiarid environments. Irrigation and ecological water were simulated by a new commercial software MIKE HYDRO. Groundwater was simulated by MODFLOW. All the outputs of theses hydrological models were taken as inputs into the DSS in three types of links： regression equations, stationary data inputs, or dynamic data inputs as the models running parallel in the simulation periods. The DSS integrates the hydrological data, geographic data, social and economic statistical data, and establishes the relationships with equations, conditional statements and fuzzy logics. The programming is realized in C＋＋. The DSS has four remarkable features：（1） editable land use maps to assist decision-making;（2） conjunctive use of surface and groundwater resources;（3） interactions among water, earth, ecosystem, and humans; and（4） links with hydrological models. The overall goal of the DSS is to combine the outputs of scientific models, knowledge of experts, and perspectives of stakeholders, into a computer-based system, which allows sustainability impact assessment within regional planning; and to understand ecosystem services and integrate them into land and water management.

Intelligent Decision Support System for Bank Loans Risk Classification

Intelligent Decision Support System (IISS) for Bank Loans Risk Classification (BLRC), based on the way of integration Artificial Neural Network (ANN) and Expert System (ES), is proposed. According to the feature of BLRC, the key financial and non-financial factors are analyzed. Meanwhile, ES and Model Base (MB) which contain ANN are designed . The general framework,interaction and integration of the system are given. In addition, how the system realizes BLRC is elucidated in detail.

A Model-Based, Aspiration-Led Decision Support System NY-IEDSS

An AI-aided simulation system embedded in a model-based, aspiration-led decision support system NY-IEDSS is reported. The NY-IEDSS is designed for mid-term development strategic study of the Nanyang Region in Henan, China, and is getting beyond its prototype stage under the decision maker's (the end user) orientation. The integration of simulation model system, decision analysis and expert system for decision support in the system implementation was reviewed. The intent of the paper is to provide insight as to how system capability and acceptability can be enhanced by this integration. Moreover, emphasis is placed on problem orientation in applying the method.

Building up Multi－Layered Perceptrons as Classifier System for Decision Support

This paper focuses on some application issues in m.multi-layered perceptrons researches. The following problem areas are discussed: (1) the classification capability of multi-layered perceptrons; (2) theself-configuration algorithm for facilitating the design of the neural nets' structure;and,finally (3) the application of the fast BP algorithm to speed up the learning procedure. Some experimental results with respect to the application of multi-layered perceptrons as classifier systems in the comprehensive evaluation of Chinese large cities are presented.

Analysis and Study of Parallel Processing Mode inVLDB Decision Support System

Nowadays, many kinds of computer network data management systems have been built widely in China. People have realized widely that management information system (MIS) has brought a revolution to the management mechanism. Moreover, the managers of company need wide-range and comprehensive decision information more and more urgently which is the character of information explosion era. The needs of users become harsher and harsher in the design of MIS, and these needs have brought new problems to the general designers of MIS. Furthermore, the current method of traditional database development can't solve so big and complex problems of wide-range and comprehensive information processing. This paper proposes the adoption of parallel processing mode, the built of new decision support system (DSS) is to discuss and analyze the problems of information collection, processing and the acquirement of full-merit information with cross-domain and cross-VLDB (very-large database).

An Investment Decision Support System for Process Industries

Most studies on investment evaluation mainly focus on enterprise economic benefits only, without process operability and sustainability considered. In this paper, we suggest that investment evaluation in process industries should be executed under three strategic objectives--enterprise benefits, social benefits and customer benefits. A systematic investment evaluation and decision-making method with a four-step procedure based on the analytic hierarchy process (AHP) is proposed to evaluate various qualitative and quantitative elements with various criteria. At the first step, the decision hierarchy is constructed under the three strategic objectives. Second, pair-wise comparison is utilized to evaluate the weights of elements and criteria. Third, qualitative elements are quantified by pair-wise comparison and quantitative elements are re-scaled by a uniform criterion. At the last, the best choice is made through synthesizing values upward in the hierarchy. An investment decision support system (DSS) is developed based on Microsoft Excel, and applied to a retrofit investment of united fluid catalytic cracking(FCC) and liquefied gas separation process in a refinery plant.