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.

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.

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.

A Knowledge Model-and Growth Model-Based Decision Support System for Wheat Management

By applying the system analysis principle and mathematical modeling technique to knowledge expression system for crop cultural management, the fundamental relationships and quantitative algorithms of wheat growth and management indices to variety types, ecological environments and production levels were analysed and extracted, and a dynamic knowledge model with temporal and spatial characters for wheat management(WheatKnow)was developed. By adopting the soft component characteristics as non language relevance , re-utilization and portable system maintenance. and by further integrating the wheat growth simulation model(WheatGrow)and intelligent system for wheat management, a comprehensive and digital knowledge model, growth model and component-based decision support system for wheat management(MBDSSWM)was established on the platforms of Visual C++ and Visual Basic. The MBDSSWM realized the effective integration and coupling of the prediction and decision-making functions for digital crop management.

Study on Decision Support System of Agricultural Sustainable Development of Jianli County in Jianghan Plain

With the agricultural development and the modernization of decision-making, it is necessary to establish the agricultural sustainable development decision support system supported by GIS. We set Jianli county as an example; our aim is to realize decision spatialiazation with the support of information system, remote sensing and artificial intelligence. The system components are described in the aspects of database, knowledge base, model-base and method-base. This system will provide a workable system for local decision-makers and agricultural management sections.

Visualized User Interface for Decision Support System

To work efficiently with DSS, most users need assistance in representation conversion, i. e., translating the specific outcome from the DSS into the universal language of visual. In generally, it is much easier to understand the results from the DSS if they are translated into charts, maps, and other scientific displays, because visualization exploits human natural ability to recognize and understand visual pattern. In this paper we discuss the concept of visualization for DSS. AniGraftool, a software system, is introduced as an example of Visualized User Interface for DSS.

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 COVID-19 Empowered with Deep Learning

The prompt spread of Coronavirus(COVID-19)subsequently adorns a big threat to the people around the globe.The evolving and the perpetually diagnosis of coronavirus has become a critical challenge for the healthcare sector.Drastically increase of COVID-19 has rendered the necessity to detect the people who are more likely to get infected.Lately,the testing kits for COVID-19 are not available to deal it with required proficiency,along with-it countries have been widely hit by the COVID-19 disruption.To keep in view the need of hour asks for an automatic diagnosis system for early detection of COVID-19.It would be a feather in the cap if the early diagnosis of COVID-19 could reveal that how it has been affecting the masses immensely.According to the apparent clinical research,it has unleashed that most of the COVID-19 cases are more likely to fall for a lung infection.The abrupt changes do require a solution so the technology is out there to pace up,Chest X-ray and Computer tomography(CT)scan images could significantly identify the preliminaries of COVID-19 like lungs infection.CT scan and X-ray images could flourish the cause of detecting at an early stage and it has proved to be helpful to radiologists and the medical practitioners.The unbearable circumstances compel us to flatten the curve of the sufferers so a need to develop is obvious,a quick and highly responsive automatic system based on Artificial Intelligence(AI)is always there to aid against the masses to be prone to COVID-19.The proposed Intelligent decision support system for COVID-19 empowered with deep learning(ID2S-COVID19-DL)study suggests Deep learning(DL)based Convolutional neural network(CNN)approaches for effective and accurate detection to the maximum extent it could be,detection of coronavirus is assisted by using X-ray and CT-scan images.The primary experimental results here have depicted the maximum accuracy for training and is around 98.11 percent and for validation it comes out to be approximately 95.5 percent while statistical parameters like sensitivity and specificity for training is 98.03 percent and 98.20 percent respectively,and for validation 94.38 percent and 97.06 percent respectively.The suggested Deep Learning-based CNN model unleashed here opts for a comparable performance with medical experts and it ishelpful to enhance the working productivity of radiologists. It could take the curvedown with the downright contribution of radiologists, rapid detection ofCOVID-19, and to overcome this current pandemic with the proven efficacy.

Research on an Intelligent Decision Support System for a Conceptual Innovation Design of Pumping Units Based on TRIZ

Attention is concentrated on how to perform the innovative design during the process of pumping unit conceptual design, and how to enhance design efficiency and inspire creativity. Aiming at the shortages of conceptual design, introducing the theory of inventive problem solving （TRIZ） into the mechanical product design for producing innovative ideas, and using the advanced computer-aided technique, the intelligent decision support system （IDSS） based on TRIZ （TRIZ-IDSS） has been constructed. The construction method, system structure, conceptual production, decisionmaking and evaluation of the problem solving subsystem are discussed. The innovative conceptual design of pumping units indicates that the system can help the engineers open up a new space of thinking, overcome the thinking inertia, and put forward innovative design concepts. This system also can offer the scientific instructions for the innovative design of mechanical products.

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.

A multi-user decision support system for online city bus tour planning

Tourism is rapidly becoming a sustainable pathway toward economic prosperity for host countries and communities. Recent advances in information and communications technology, the smartphone, the Internet and Wi-Fi have given a boost to the tourism industry. The city bus tour （CBT） service is one of the most successful businesses in the tourism industry. However, there exists no smart decision support system determining the most efficient way to plan the itinerary of a CBT. In this research, we report on the ongoing development of a mobile application （app） and a website for tourists, hoteliers and travel agents to connect with city bus operators and book/purchase the best CBT both in terms of cost and time. Firstly, the CBT problem is formulated as an asymmetric sequential three-stage arc routing problem. All places of interest （PoI） and pickup/dropout points are identified with arcs of the network （instead of nodes）, each of which can be visited at least once （instead of exactly once）. Secondly, the resulting pure integer programming （IP） problem is solved using a leading optimization soft- ware known as General Algebraic Modeling System （GAMS）. The GAMS code developed for this project returns： （1） the exact optimal solution identifying the footprints of the city bus relative to all the arcs forming the minimal cost network; （2） the augmenting paths corre- sponding to the pickup stage, the PoI visiting stage and the drop-off stage. Finally, we demonstrate the applicability of the mobile app/website via a pilot study in the city of Melbourne （Australia）. All the computations relative to the initial tests show that the ability of the app to answer users＇ inquiries in a fraction of a minute.

Construction of the Intelligent and Multiple Object Decision Support System for Making Train Diagram

Multiple objects decision is used widely in many complex fields. In this paper an idea is provided to construct a train diagram intelligent multiple objects decision support system (TDIMODSS). And the reference point method is used to solve the complicated and large scale problems of making and adjusting train schedule. This paper focuses on the principle and framework of the model base, knowledge base of train diagram. It is shown that the TDIMODSS can solve the problems and their uncertainty in making train diagram, and can combine the expert knowledge, experience and judgement of a decision maker into the system. In addition to that, a friendly working environment is also presented, which brings together the human judgement, the adaptability to environment and the computerised information.

Decision Support System for Maintenance Management Using Bayesian Networks

The maintenance process has undergone several major developments that have led to proactive considerations and the transformation fiom the traditional ＂fail and fix＂ practice into the ＂predict and prevent＂ proactive maintenance methodology. The anticipation action, which characterizes this proactive maintenance strategy is mainly based on monitoring, diagnosis, prognosis and decision-making modules. Oil monitoring is a key component of a successful condition monitoring program. It can be used as a proactive tool to identify the wear modes of rubbing pans and diagnoses the faults in machinery. But diagnosis relying on oil analysis technology must deal with uncertain knowledge and fuzzy input data. Besides other methods, Bayesian Networks have been extensively applied to fault diagnosis with the advantages of uncertainty inference; however, in the area of oil monitoring, it is a new field. This paper presents an integrated Bayesian network based decision support for maintenance of diesel engines.

A decision framework for identifying models to estimate forest ecosystem services gains from restoration

Restoring degraded forests and agricultural lands has become a global conservation priority. A growing number of tools can quantify ecosystem service tradeoffs associated with forest restoration. This evolving ＂tools landscape＂ presents a dilemma： more tools are available, but selecting appropriate tools has become more challenging. We present a Restoration Ecosystem Service Tool Selector （RESTS） framework that describes key characteristics of 13 ecosystem service assessment tools. Analysts enter information about their decision context, services to be analyzed, and desired outputs. Tools are filtered and presented based on five evaluative criteria： scalability, cost, time requirements, handling of uncertainty, and applicability to benefit-cost analysis. RESTS uses a spreadsheet interface but a web-based interface is planned. Given the rapid evolution of ecosystem services science, RESTS provides an adaptable framework to guide forest restoration decision makers toward tools that can help quantify ecosystem services in support of restoration.

Modelling an Efficient Clinical Decision Support System for Heart Disease Prediction Using Learning and Optimization Approaches

With the worldwide analysis,heart disease is considered a significant threat and extensively increases the mortality rate.Thus,the investigators mitigate to predict the occurrence of heart disease in an earlier stage using the design of a better Clinical Decision Support System(CDSS).Generally,CDSS is used to predict the individuals’heart disease and periodically update the condition of the patients.This research proposes a novel heart disease prediction system with CDSS composed of a clustering model for noise removal to predict and eliminate outliers.Here,the Synthetic Over-sampling prediction model is integrated with the cluster concept to balance the training data and the Adaboost classifier model is used to predict heart disease.Then,the optimization is achieved using the Adam Optimizer(AO)model with the publicly available dataset known as the Stalog dataset.This flowis used to construct the model,and the evaluation is done with various prevailing approaches like Decision tree,Random Forest,Logistic Regression,Naive Bayes and so on.The statistical analysis is done with theWilcoxon rank-summethod for extracting the p-value of the model.The observed results show that the proposed model outperforms the various existing approaches and attains efficient prediction accuracy.This model helps physicians make better decisions during complex conditions and diagnose the disease at an earlier stage.Thus,the earlier treatment process helps to eliminate the death rate.Here,simulation is done withMATLAB 2016b,and metrics like accuracy,precision-recall,F-measure,p-value,ROC are analyzed to show the significance of the model.