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.

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

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ANINTELLIGENT DECISION SUPPORTSYSTEM（IDSS） ONTHEPROCESSOFNICKELMATTESMELTER

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An Intelligence Decision Support System for Diagnosis and Management of Grain and Cotton Crop Pests in Xinjiang

Xinjiang Uygur Autonomous Region is a scarcely populated area in China and technicians for plant protection are extremely deficient.The occurrence areas of insect pests in grain and cotton crops have been increasing year by year, causing serious economic losses. Aiming for several main grain and economic crops of Xinjiang(cotton, corn and wheat), an intelligence decision support system for diagnosis and management of grain and cotton crop pests in Xinjiang was designed and developed, which was based on android platform and windows system architecture. APP application program of smart phones was used as an implementation form. The intelligence decision support system will help plant protection personnel and farmers to solve local pest recognition and prevention control problem at the grassroots level in Xinjiang remote regions.

Decision Making Support System(DSS)——A Synthesized and Integrated Crystallization of Systems Engineering, Artificial Intelligence and Electronic Technologies

I firmly believe that of systems engineering is the requirement-driven force for the progress ofsoftware engineering, artificial intelligence and electronic technologies. The development ofsoftware engineering, artificial intelligence and electronic technologies is the technical supportfor the progress of systems engineering. INTEGRATION can be considered as "bridging" the ex-isting technologies and the People together into a coordinated SYSTEM.

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.

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.

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.

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.

A Framework for Intelligent Decision Support System for Traffic Congestion Management System

Traffic congestion problem is one of the major problems that face many transportation decision makers for urban areas. The problem has many impacts on social, economical and development aspects of urban areas. Hence the solution to this problem is not straight forward. It requires a lot of effort, expertise, time and cost that sometime are not available. Most of the existing transportation planning software, specially the most advanced ones, requires personnel with lots practical transportation planning experience and with high level of education and training. In this paper we propose a comprehensive framework for an Intelligent Decision Support System (IDSS) for Traffic Congestion Management System that utilizes a state of the art transportation network equilibrium modeling and providing an easy to use GIS-based interaction environment. The developed IDSS reduces the dependability on the expertise and level of education of the transportation planners, transportation engineers, or any transportation decision makers.

Scientific Train of Thought and Methodological Innovation in the Intelligent Decision Support System for Earthquake Prediction in China

The level of present understanding of earthquake prediction of seismologists at home and abroad is very different. This is because China has opened up a special path of earthquake prediction research that has not been explored by other countries, with its own advantages and potentialities.Therefore, we considered that it is the most practical way to use the advantages and potentialities for raising the earthquake prediction level. For this purpose, we have developed a set of intelligent decision support system for earthquake prediction, with the analysis of cluster anomalies process at the core. The facts show that it can obviously raise the level of synthetic earthquake prediction.

Development of a Simulation-Based Intelligent Decision Support System for the Adaptive Real-Time Control of Flexible Manufacturing Systems

This paper describes a simulation-based intelligent decision support system (IDSS) for real time control of a flexible manufacturing system (FMS) with machine and tool flexibility. The manufacturing processes involved in FMS are complicated since each operation may be done by several machining centers. The system design approach is built around the theory of dynamic supervisory control based on a rule-based expert system. The paper considers flexibility in operation assignment and scheduling of multi-purpose machining centers which have different tools with their own efficiency. The architecture of the proposed controller consists of a simulator module coordinated with an IDSS via a real time event handler for implementing inter-process synchronization. The controller’s performance is validated by benchmark test problem.

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.

Intelligent decision support system of operation-optimization in copper smelting converter

An artificial intelligence technique was applied to the optimization of flux adding systems and air blasting systems, the display of on line parameters, forecasting of mass and compositions of slag in the slagging period, optimization of cold material adding systems and air blasting systems, the display of on line parameters, and the forecasting of copper mass in the copper blow period in copper smelting converters. They were integrated to build the Intelligent Decision Support System of the Operation Optimization of Copper Smelting Converter(IDSSOOCSC), which is self learning and self adaptating. Development steps, monoblock structure and basic functions of the IDSSOOCSC were introduced. After it was applied in a copper smelting converter, every production quota was clearly improved after IDSSOOCSC had been run for 4 months. Blister copper productivity is increased by 6%, processing load of cold input is increased by 8% and average converter life span is improved from 213 to 235 furnace times.

Intelligent decision support platform of new energy vehicles

New energy vehicles(NEVs) are gaining wider acceptance as the transportation sector is developing more environmentally friendly and sustainable technology. To solve problems of complex application scenarios and multi-sources heterogenous data for new energy vehicles and weak platform scalability,the framework of an intelligent decision support platform is proposed in this paper. The principle of software and hardware system is introduced. Hadoop is adopted as the software system architecture of the platform. Master-standby redundancy and dual-line redundancy ensure the reliability of the hardware system. In addition, the applications on the intelligent decision support platform in usage patterns recognition, energy consumption, battery state of health and battery safety analysis are also described.

A knowledge-based decision support system for engineering design

A knowledge-based decision supporting system, used for engineering design is introduced by describing the architecture, function, workflow of the system and its way of implementation. Based upon information composed of knowledge, models, data, cases, methods, etc, the system is designed to use such methods as knowledge-based reasoning, case-based reasoning, and multi-criteria evaluation techniques to provide effective tools to support the decision-making process.

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.

PROBLEM-ORIENTED INFORMATION RETRIEVAL DECISION SUPPORT SYSTEMS

Decision Support Systems(DSS)are man-machine interaction systems,which support the de-cision-makers to solve the unstructured and semi-structured decisions,this paper advances that thefunction of problem-oriented information retrieval DSS can meet the needs of enterprise’s topmanagement effectively in comparison with other information retrieval functions,in accordancewith the features of supporting information for decision.An architecture of this system is presented,which dissolves a problem put forward or recognized by the user into the problem recognized by thecomputer,forming retrieval tactics and searching the data the user needs.Designed and developedaccording to the architecture of this system,a prototype system is introduced,which is CF Econom-ic Environment Information Retrieval DSS.

Energy Efficient Cluster Based Clinical Decision Support System in IoT Environment

Internet of Things(IoT)has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded devices.The e-healthcare application solely depends on the IoT and cloud computing environment,has provided several characteristics and applications.Prior research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing,which led to quick exhaustion of energy.In this view,this paper introduces a new energy efficient cluster enabled clinical decision support system(EEC-CDSS)for embedded IoT environment.The presented EECCDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic process.The EEC-CDSS model incorporates particle swarm optimization with levy distribution(PSO-L)based clustering technique,which clusters the set of IoT devices and reduces the amount of data transmission.In addition,the IoT devices forward the data to the cloud where the actual classification procedure is performed.For classification process,variational autoencoder(VAE)is used to determine the existence of disease or not.In order to investigate the proficient results analysis of the EEC-CDSS model,a wide range of simulations was carried out on heart disease and diabetes dataset.The obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.