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.

Automated decision support for Hallux Valgus treatment options using anteroposterior foot radiographs

BACKGROUND Assessment of the potential utility of deep learning with subsequent image analysis to automate the measurement of hallux valgus and intermetatarsal angles from radiographs to serve as a preoperative aid in establishing hallux valgus severity for clinical decision-making.AIM To investigate the accuracy of automated measurements of angles of hallux valgus from radiographs for further integration with the preoperative planning process.METHODS The data comprises 265 consecutive digital anteroposterior weightbearing foot radiographs.181 radiographs were utilized for training(161)and validating(20)a U-Net neural network to achieve a mean Sørensen–Dice index>97%on bone segmentation.84 test radiographs were used for manual(computer assisted)and automated measurements of hallux valgus severity determined by hallux valgus(HVA)and intermetatarsal angles(IMA).The reliability of manual and computerbased measurements was calculated using the interclass correlation coefficient(ICC)and standard error of measurement(SEM).Inter-and intraobserver reliability coefficients were also compared.An operative treatment recommendation was then applied to compare results between automated and manual angle measurements.RESULTS Very high reliability was achieved for HVA and IMA between the manual measurements of three independent clinicians.For HVA,the ICC between manual measurements was 0.96-0.99.For IMA,ICC was 0.78-0.95.Comparing manual against automated computer measurement,the reliability was high as well.For HVA,absolute agreement ICC and consistency ICC were 0.97,and SEM was 0.32.For IMA,absolute agreement ICC was 0.75,consistency ICC was 0.89,and SEM was 0.21.Additionally,a strong correlation(0.80)was observed between our approach and traditional clinical adjudication for preoperative planning of hallux valgus,according to an operative treatment algorithm proposed by EFORT.CONCLUSION The proposed automated,artificial intelligence assisted determination of hallux valgus angles based on deep learning holds great potential as an accurate and efficient tool,with comparable accuracy to manual measurements by expert clinicians.Our approach can be effectively implemented in clinical practice to determine the angles of hallux valgus from radiographs,classify the deformity severity,streamline preoperative decision-making prior to corrective surgery.

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.

Light-Weighted Decision Support Framework for Selecting Cloud Service Providers

Multi-criteria decision making(MCDM)is a technique used to achieve better outcomes for some complex business-related problems,whereby the selection of the best alternative can be made in as many cases as possible.This paper proposes a model,the multi-criteria decision support method,that allows both service providers and consumers to maximize their profits while preserving the best matching process for resource allocation and task scheduling.The increasing number of service providers with different service provision capabilities creates an issue for consumers seeking to select the best service provider.Each consumer seeks a service provider based on various preferences,such as price,service quality,and time to complete the tasks.In the literature,the problem is viewed from different perspectives,such as investigating how to enhance task scheduling and the resource allocation process,improve consumers’trust,and deal with network problems.This paper offers a novel model that considers the preferences of both service providers and consumers to find the best available service provider for each consumer.First,the model adopts the best-worst method(BWM)to gather and prioritize tasks based on consumers’and service providers’preferences.Then,the model calculates and matches similarities between the sets of tasks from the consumer’s side with the sets of tasks from the provider’s side to select the best service provider for each consumer using the two proposed algorithms.The complexity of the two algorithms is found to be O(n3).

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.

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.

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.

An Intelligent Decision Support System Generator

The focus of this paper is on a new concept framework and an architecture of an intelligent decision support syetem generator (DSSG). The framework results from a synthesis of two existing frameworks: Spragae and Bonczek, while the architecture is a rooted partial order network. From our experience which comes out of the project of DSSG, we consider that they are keys of further research and development of DSS.

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.

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.

Method for designing organization decision support system framework

The concept of organization decision support system （ODSS） is defined according to practical applications and novel understanding. And a framework for ODSS is designed. The framework has three components： infrastructure, decision-making process and decision execution process. Infrastructure is responsible to transfer data and information. Decision-making process is the ODSS＇s soul to support decision-making. Decision execution process is to evaluate and execute decision results derived from decision-making process. The framework presents a kind of logic architecture. An example is given to verify and analyze the framework. The analysis shows that the framework has practical values, and has also reference values for understanding ODSS and for theoretical studies.

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.

DEVELOPING EXPERT DECISION SUPPORT SYSTEM WTH VP-EXPERT AND LOTUS 1-2-3 IN RE-BLENDING BY USE

An expert decision support system (EDSS) for multi-bins balance and contro1 of orequality in production ore bins of some large-scale open pit iron mine in China has been developed byexpert svitem tool software VP-EXPERT and integration software LOTUS 1-2-3 in this paper. Itis known by practicing that a medium-scale EDSS constructed on microcomputer is completcly, feaasible by means of VP-EXEPERT to construct knowledge base system (KBS), LOTUS 1-2-3 tomake decision support system (DSS) and link them with BAT.

A Decision Support Model for Predicting Avoidable Re-Hospitalization of Breast Cancer Patients in Kenyatta National Hospital

This study aimed to develop a clinical Decision Support Model (DSM) which is software that provides physicians and other healthcare stakeholders with patient-specific assessments and recommendation in aiding clinical decision-making while discharging Breast cancer patient since the diagnostics and discharge problem is often overwhelming for a clinician to process at the point of care or in urgent situations. The model incorporates Breast cancer patient-specific data that are well-structured having been attained from a prestudy’s administered questionnaires and current evidence-based guidelines. Obtained dataset of the prestudy’s questionnaires is processed via data mining techniques to generate an optimal clinical decision tree classifier model which serves physicians in enhancing their decision-making process while discharging a breast cancer patient on basic cognitive processes involved in medical thinking hence new, better-formed, and superior outcomes. The model also improves the quality of assessments by constructing predictive discharging models from code attributes enabling timely detection of deterioration in the quality of health of a breast cancer patient upon discharge. The outcome of implementing this study is a decision support model that bridges the gap occasioned by less informed clinical Breast cancer discharge that is based merely on experts’ opinions which is insufficiently reinforced for better treatment outcomes. The reinforced discharge decision for better treatment outcomes is through timely deployment of the decision support model to work hand in hand with the expertise in deriving an integrative discharge decision and has been an agreed strategy to eliminate the foreseeable deteriorating quality of health for a discharged breast cancer patients and surging rates of mortality blamed on mistrusted discharge decisions. In this paper, we will discuss breast cancer clinical knowledge, data mining techniques, the classifying model accuracy, and the Python web-based decision support model that predicts avoidable re-hospitalization of a breast cancer patient through an informed clinical discharging support model.

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.

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.

Disaster Reduction Decision Support System Against Debris Flows and Landslides Along Highway in Mountainous Area

Highways in mountainous areas are easy to be damaged by such natural disasters as debris flows and landslides and disaster reduction decision support system (DRDSS) is one of the important means to mitigate these disasters. Guided by the theories and technologies of debris flow and landslide reduction and supported by geographical information system (GIS), remote sensing and database techniques, a DRDSS against debris flow and landslide along highways in mountainous areas has been established on the basis of such principles as pertinence, systematicness, effectiveness, easy to use, open and expandability. The system consists of database, disaster analysis models and decisions on reduction of debris flows and landslides, mainly functioning to zone disaster dangerous degree, analyze debris flow activity, simulate debris flow deposition and diffusion, analyze landslide stability, select optimal highway renovation scheme and plan disaster prevention and control engineering. This system has been applied successfully to the debris flow and landslide treatment works along Palongzangbu Section of Sichuan-Tibet Highway.