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Improved Metaheuristics with Machine Learning Enabled Medical decision support system

Computers, Materials & Continua 2022年第11期73卷 2423-2439页

作者： Sara A.Althubiti JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Romany F.Mansour Fayadh Alenezi Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Research Center-CIENS Escuela Naval de Suboficiales A.R.C.“Barranquilla”BarranquillaColombia Electronics and Telecommunications Engineering Program Universidad Autónoma del CaribeBarranquilla080001Colombia Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080001Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga 72511Egypt Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and others.Besides,the latest advances of Artificial Intelligence(AI)tools find helpful for decision-making in innovative healthcare to diagnose several diseases.Ovarian Cancer(OC)is a kind of cancer that affects women’s ovaries,and it is tedious to identify OC at the primary stages with a high mortality rate.The OC data produced by the Internet of Medical Things(IoMT)devices can be utilized to differentiate OC.In this aspect,this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system(QBWO-MLDSS)for smart healthcare.The primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and accurately.Besides,the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the data.In addition,the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature subsets.Moreover,symbiotic organisms search(SOS)with extreme learning machine(ELM)model is applied as a classifier for the detection and classification of ELM model,thereby improving the overall classification performance.The design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s novelty.The experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset,and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.

关键词： Ovarian cancer decision support system smart healthcare IoMT deep learning feature selection

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Artificial Intelligence Enabled decision support system on E-Healthcare Environment

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Intelligent Automation & Soft Computing 2023年第5期36卷 2299-2313页

作者： B.Karthikeyan K.Nithya Ahmed Alkhayyat Yousif Kerrar Yousif Department of Information Technology Panimalar Engineering CollegeChennai600123India Department of Information Technology Dr.MGR Educational and Research InstituteChennai600095India College of Technical Engineering The Islamic UniversityNajafIraq Department of Computer Technical Engineering Al-Hadba University CollegeMosulIraq

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.

关键词： E-healthcare decision support system cardiovascular disease feature selection deep learning

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decision support system for the mobile volcano fast response system

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International Journal of Digital Earth 2010年第3期3卷 280-291页

作者： S.Bernsdorf R.Barsch M.Beyreuther K.Zakšek M.Hort J.Wassermann CentauronGeosoftware&Consulting JenaGermany Department of Earth and Environmental Sciences Ludwig-Maximilians-UniversityMunichGermany Institute of Geophysics University of HamburgHamburgGermany

A mobile volcano fast response system(VFRS)that can be used for volcano monitoring in case of volcanic unrest anywhere in the world is currently under development in Germany.The main goal of the project called Exupéry is to provide the communication technology for stations in the field and an expert system that collects all data from various sources,assembles them in a database,and allows users to assess the data through one common web GIS interface.The system also includes an integrated automatic alert level including the alert level estimation in order to characterize the activity state of the volcano.The web GIS interface serves as a decision support system to assist scientists and local authorities in deciding how to react in the case of volcanic unrest.

关键词： volcanic unrest decision support system web GIS interface automatic alert level Exupéry VFRS

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decision support system for emergency scheduling of raw water supply systems with multiple sources

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Frontiers of Environmental Science & Engineering 2013年第5期7卷 777-786页

作者： Qi WANG Shuming LIU Wenjun LIU Zoran KAPELAN Dragan SAVIC School of Environment Tsinghua University Beijing 100084 China Centre for Water systems College of Engineering Mathematics and Physical Sciences University of Exeter Exeter Devon EX4 4QF UK

A hydraulic model-based emergency schedul- ing decision support system （DSS） is designed to eliminate the impact of sudden contamination incidents occurring upstream in raw water supply systems with multiple sources. The DSS consists of four functional modules, including water quality prediction, system safety assessment, emergency strategy inference and scheduling optimization. The work flow of the DSS is as follows. First, the water quality variations on specific cross-sections are calculated given the pollution information. Next, a comprehensive evaluation on the safety of the current system is conducted using the outputs in the first module. This will assist in the assessment of whether the system is in danger of failure, taking both the impact of pollution and system capacity into account. If there is a severe impact of contamination on the reliability of the system, a fuzzy logic based inference module is employed to generate reason- able strategies including technical measures. Otherwise, a Genetic Algorithm （GA）-based optimization model will be used to find the least-cost scheduling plan. The proposed DSS has been applied to a coastal city in South China during a saline tide period as validation. Through scenario analysis, it is demonstrated that this DSS tool is instrumental in emergency scheduling for the water company to quickly and effectively respond to sudden contamination incidents.

关键词： decision support system raw water supply system contamination incident emergency scheduling,hydraulic model safety assessment

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A decision support system for Economic Development and Price Reform

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Journal of systems Engineering and Electronics 1993年第1期4卷 65-73页

作者： Hu Lequn, Yang Shetang and Zhang WenzhongInstitute of system Science, Academia Sinica, Beijing, 100080Zhu JunNational Information Center of China, Beijing, 100072 Institute of system Science Academia Sinica Beijing 100080 National Information Center of China Beijing 100072

In this paper, we present a real decision support system applied to China's macroeconomic management. The structure, design and functions of the system are discussed, and the problems occurring in designing and making... 详细信息

关键词： decision support system system analysis Interactive method Macroeconomic management Price reform.

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A decision support system based on machined learned Bayesian network for predicting successful direct sales marketing

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Journal of Management Analytics 2021年第2期8卷 295-315页

作者： Seyedmohsen Hosseini Industrial Engineering Technology University of Southern MississippiMississippiUSA

This paper proposes a decision support system based on a machine-learned Bayesian network(BN)to predict the success rate of telemarketing calls for long-term bank deposits.Telemarketing is one of the most common interactive techniques of direct marketing,widely used by financial institutions such as banks to sell long-term deposits.In this study,we develop a BN model that predicts the likelihood that a potential client subscribes to a long-term deposit,which is considered an output variable.The causal relationship among client attributes and outcomes has been identified using the augmented Naïve Bayes approach,a well-known supervised learning algorithm.The impact of each client’s attribute on the likelihood of subscribing is predicted.Further,we carry out multiple simulation scenarios using BN’s unique features(forward and backward propagation)to provide more in-depth discussions and analysis on predicting the likelihood of subscription for clients with particular characteristics.

关键词： decision support system prediction sales marketing decision making

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A peer-to-peer logic environment to validate flashing yellow arrow decision support system

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Journal of Traffic and Transportation Engineering(English Edition) 2021年第5期8卷 735-750页

作者： Hatem Abou-Senna Essam Radwan Hesham Eldeeb Center for Advanced Transportation systems Simulations(CATSS) Department of CivilEnvironmental and Construction Engineering(CECE)University of Central FloridaOrlandoFL 32816USA Deep Numerics LLC.OrlandoFL 32820USA

The flashing yellow arrow(FYA)signal display creates an opportunity to enhance the leftturn phase with a variable mode that can be changed on demand.This research develops an integrated general purpose data collection module that time stamps detector and phase state changes within a National Electrical Manufacturers Association(NEMA)actuated traffic signal controller to provide recommendations for the flashing yellow arrow left-turn mode on a cycle-by-cycle basis.115 left-turn approaches at 38 intersections with locations across the State of Florida were analyzed totaling 1370 h of video data processed including off-peak and peak conditions.Video data extraction was an essential step in developing thegap thresholds for the permissive left-turn.Actual intersection field data were obtained through loop detector mapping to the controller in the lab in real-time mode using a peerto-peer logic environment.A custom communications software was developed to retrieve instantaneous channel input data,synchronize opposing through green phase,analyze traffic information,provide the algorithm decision,and generate a real-time log recording the events to determine whether it would be optimal to switch the red arrow to a flashing yellow arrow.The algorithm determines the time interval between the successive arrivals of vehicles and computes the corresponding headway for each lane by cycle on a secondby-second basis.Peer-to-peer logic is a necessary step to verify and validate new traffic concepts prior to field-testing and offers the advantage of acquiring and analyzing realtime traffic data coupled with video feed with the benefit of a safe environment.

关键词： Traffic engineering Peer-to-peer logic Flashing yellow arrow decision support system Permissive left-turn Four section head

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An Intelligent decision support system Generator

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Journal of systems Engineering and Electronics 1993年第1期4卷 45-52页

作者： Shi Zhenxia and Lu JukangDept. of Computer Science, Shanghai Univ. of Science and Technology, Shanghai 201800, P.R. China Dept. of Computer Science Shanghai Univ. of Science and Technology Shanghai 201800 P.R. China

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.

关键词： decision support system decision support system generator Intelligent decision support system AI technology Hypertext technology.

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BMP decision support system for evaluating stormwater management alternatives

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Frontiers of Environmental Science & Engineering 2009年第4期3卷 453-463页

作者： Mow-Soung CHENG Jenny X.ZHEN Leslie SHOEMAKER Department of Environmental Resources Prince George’s County Government9400 Peppercorn PlaceLargo MD 20774USA Tetra Tech Inc.10306 Eaton PlaceFairfax VA 22030USA

Prince George’s County,Maryland,in the Washington D.C.metropolitan area has developed a best management practice decision support system(BMPDSS)to support analysis and decision making for stormwater management planning and design at both the site scale and the watershed levels.This paper presents a detailed description of the BMPDSS.A case study that demonstrates the application of the system is also included.The case study involves a Green Highway project located in a highly urbanized area within the Anacostia River watershed of the county.Several best management practices(BMP)such as bioretention,filter vegetative swale,porous paving,and landscape infiltration are proposed for reducing highway runoff and improving water quality.The BMPDSS is used to identify and evaluate various alternatives to determine the most costeffective types and combinations of BMPs that minimize the highway runoff pollution.

关键词： best management practices decision support system low impact development geographic information system optimization

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

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Journal of Arid Land 2018年第2期10卷 169-182页

作者： YU Yang CHEN Xi Philipp HUTTNER Marie HINNENTHAL Andreas BRIEDEN SUN Lingxiao Markus DISSE Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Chair of Hydrology and River Basin Management Technical University of Munich Chair of Statistics and Risk Management Universitaet der Bundeswehr Muenchen

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.

关键词： decision support system hydrological modeling ecosystem services land management socio-economic indicator Tarim River Basin

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