Enhanced Decision Support System Research Articles

Enhanced Decision Support System for Automated Fish Feeder and Water Quality Detection with SMS Notification

Enhanced Decision Support System for Automated Fish Feeder and Water Quality Detection with SMS Notification

Enhanced decision support system to predict and prevent hypertension using computational intelligence techniques

Medical decision support systems have been a core of intense research for years. The ongoing study shows that artificial intelligence has been accustomed to probe risk factors for hypertension. Factors, like health-damaging personal behaviors and changes in lifestyle and environment, are major contributors to chronic diseases. The goal of this research was to forecast the risk of developing hypertension by revealing hidden patterns in medical datasets. Quality of the data is the key to enhance the performance of learning model. But most healthcare data suffer from class imbalance problem, which induce the need for an intelligent model which can learn from such grimy data. This paper incorporates a novel approach by combing learning model and rule-based mining to offer decision support. Typically, the proposed work comprises two main implications. First suggests an intelligent learning model using boosting-based support vector machine to diagnose and expose multi-class categories in the imbalanced datasets. Finally, the enhanced predictive model is built upon the classification solution which will portray the innate data similarities. An intelligent fuzzy-based approach was employed to recognize frequent behavioral patterns. Based on these rules, valid decisions could be made to prevent hypertension. The suggested enhanced model is evaluated using a real-time hypertension dataset obtained through primary health centers. With the combination of ensemble strategies, the proposed intelligent learning model attains high classification accuracy for the imbalanced dataset above the traditional model. Thus, the efficient integration of personalized behavior with health data could provide a better understanding regarding patient health. In future this can serve as an eye toward personalized medicine.

Advanced Connection Manager for Interworking between 3GPP and Non-3GPP Networks

Smart phones and handheld devices equip with multiple radio access technologies capabilities e.g. cellular and Wi-Fi now a days. It is observed that seamless session and service continuity is a difficult task while device is roaming across different radio access technologies. Connection Manager, ANDSF and Traffic Steering Algorithms are mainly used to address such switching requirements. Radio access selection decision is usually based upon various parameters like network capabilities, availability, quality, subscription and policies. This research compares network quality of all available networks for the duration and then connection manager’s decision support system ranks all suitable networks to select most appropriate access network. This paper investigates about new input parameters to be included in connection manager’s enhanced decision support system for the smarter decision of access network selection.

The Impact of Cloud Computing on Decision Support System in Adopting Knowledge Management in Saudi Organizations

Background/Objectives: Due to enormous increase in the volume of the information within and outside of the organizations, traditional business and information models are unable to cater to the needs of the modern business needs. Methods/Findings: In this context, Cloud Computing (CC) is gaining rapid popularity amongst businesses due to its ability to handle technical and organisational facets. Cloud Computing has evolved as a reliable technology, offering enhanced Decision Support Systems (DSS) at a low cost for the organizations. CC has displayed its capability of handling large volumes of data and providing seamless services to the companies. This research explored the impact of CC on DSS and Knowledge Management (KM) on Saudi organizations. The major achievement of this research was to fill the gap in knowledge by researching the impact of integration of CC, DSS and KM for Saudi organizations. The key finding of this research is that there is a significant positive impact of CC on DSS and KM in Saudi organizations. Based on this extensive research, we strongly believe that by taking recommended precautionary measures, security concerns about CC could be overcome resulting in benefits outweighing the risks for Saudi organizations. Application: Based on the literature and qualitative research, we recommend that Saudi organizations should consider migrating from traditional business models to Cloud Computing to stay competitive in this globalised world. Keywords: Cloud Computing, Decision Support Systems, Knowledge Management

Enhanced Decision Support System Applications Based on Complex Intuitionistic Fuzzy Soft Expert Sets Theory

Decision making is one of the important milestones in the development of various real-life aspects. Decision Support System (DSS) is a computerized information system used to enhance decision-making in several areas. This study aims to improve DSS applications by extending two concepts of complex intuitionistic fuzzy sets and soft expert sets to propose the theory of complex intuitionistic fuzzy soft expert sets and defines some related concepts pertaining to this notion as well as the basic operations on this concept. Moreover, the algorithm for such concept is developed.

Enhanced Decision Support Systems in Intensive Care Unit Based on Intuitionistic Fuzzy Sets

In areas of medical diagnosis and decision-making, several uncertainty and ambiguity shrouded situations are most often imposed. In this regard, one may well assume that intuitionistic fuzzy sets (IFS) should stand as a potent technique useful for demystifying associated with the real healthcare decision-making situations. To this end, we are developing a prototype model helpful for detecting the patients risk degree in Intensive Care Unit (ICU). Based on the intuitionistic fuzzy sets, dubbed Medical Intuitionistic Fuzzy Expert Decision Support System (MIFEDSS), the shown work has its origins in the Modified Early Warning Score (MEWS) standard. It is worth noting that the proposed prototype effectiveness validation is associated through a real case study test at the Polyclinic ESSALEMA cited in Sfax, Tunisia. This paper does actually provide some practical initial results concerning the system as carried out in real life situations. Indeed, the proposed system turns out to prove that the MIFEDSS does actually display an imposing capability for an established handily ICU related uncertainty issues. The performance of the prototypes is compared with the MEWS standard which exposed that the IFS application appears to perform highly better in deferring accuracy than the expert MEWS score with higher degrees of sensitivity and specificity being recorded.

The socio-environmental data explorer (SEDE): a social media–enhanced decision support system to explore risk perception to hazard events

ABSTRACTSocial media are increasingly recognized as a useful data source for understanding social response to hazard events in real time and in post-event analysis. This article establishes social media–enhanced decision support systems (SME-DSS) as a synergistic integration of social media and decision support systems (DSSs) to provide structured access to native, near real-time data from a large and diverse population to assess social response to social, environmental, and technological risk and hazard events. We introduce a prototype SME-DSS entitled socio-environmental data explorer (SEDE) to explore the opportunities and challenges of leveraging social media for decision support. We use a winter storm during 25–28 January 2015 that accumulated record amounts of snow along the East Coast of the United States as a case study to evaluate SEDE in helping assess social response to environmental risk and hazard events as well as evaluate social media as a theoretical component within the social amplification of risk framework (SARF) that serves as a theoretical foundation for SME-DSS.

Knowledge network modelling to support decision-making for strategic intervention in IT project-oriented change management

This paper focuses on knowledge management to enhance decision support systems for strategic intervention in information technology (IT) project-oriented change management. It proposes a model of change management knowledge networks (CMKNM) to support decision by tackling three existing issues: insufficient knowledge traceability based on the relationships between knowledge elements and key factors, lack of procedural knowledge to provide adequate policies to guide changes, and lack of ‘lessons learned’ documentation in knowledge bases. A qualitative method was used to investigate issues surrounding knowledge mobilisation and knowledge networks. Empirical study was undertaken with industries to test the CMKNM. Results are presented from the empirical study on the key factors influencing knowledge mobilisation in IT project-oriented change management, knowledge networks and connections. The CMKNM model allows key knowledge mobilisation factors to be aligned with each other; it also defines the connections between knowledge networks allowing knowledge to be mobilised by tracing knowledge channels to support decision.

Ambient intelligence environments for accessing building information

Purpose– Healthcare facility managers work in complex and dynamic environments where critical decisions are constantly made. Providing them with enhanced decision support systems would result in a positive impact on the productivity and success of the projects they undertake, as well as the sustainability of critical healthcare infrastructure. The purpose of this paper is to propose a conceptual ambient intelligent environment for enhancing the decision-making process of the facility managers. This low-cost data-rich environment would use building information modeling (BIM) and mobile augmented reality (MAR) as technological bases for the natural human-computer interfaces and aerial drones as technological tools.Design/methodology/approach– This paper presents a scenario for the integration of augmented reality (AR) and building information modeling (BIM) to build an ambient intelligent (AmI) environment for facility managers where mobile, natural, user interfaces would provide the users with required data to facilitate their critical decision-making process. The technological requirements for having such an intelligent environment are also discussed.Findings– The proposed BIM-MAR-based approach is capable of enhancing maintenance related practices for facility managers who are mobile to integrate with their facilities' intelligent environment. This approach is also capable of providing a collaborative environment in which different stakeholders, across geographically distributed areas, could work together to solve facility management tasks.Originality/value– In this paper ambient intelligence will be considered for the first time in the area of healthcare facility management practices to provide facility managers with an intelligent BIM-based environment to access facility information and consequently enhance their decision-making process.

Decision support systems and the coordination of supply consortium partners

Abstract We propose to enhance decision support systems (DSS) by coordination capabilities to align the decision making of consortium partners for improving the process quality in volatile process environments. In computer simulation experiments, we reveal conceptional shortcomings of traditional DSS if unforeseen process-threatening events like unexpected workloads have to be handled by a consortium. It is demonstrated for a transportation process planning scenario that a process quality increase is achieved if a DSS is extended by components that align the process-related decision making of legally independent supply consortium partners. The central idea proposed to add coordination capabilities to DSS is to temporarily adjust decision models of subordinate consortium partners to the fulfillment degree of consortium objectives in order to establish a coordinated decision making.

An enhanced supervised spatial decision support system of image classification: consideration on the ancillary information of paddy rice area

The analysis, measurement, and computation of remote sensing images often require an enhanced supervised classification technique to develop an efficient spatial decision support system. Rice is a crop of global importance, which has drawn a great interest in using remote sensing techniques for evaluating its production. Ancillary information is widely used to improve the classification accuracy of satellite images. However, few of these studies questioned the importance and strategies of using this ancillary information. The enhanced decision support system in our study has two stages. In the first stage, the images are obtained from the remote sensing technique and the ancillary information is employed to increase the accuracy of classification. In the second stage, it is decided to construct an efficiently supervised classifier, which is used to evaluate the ancillary information. Back-propagation neural network (BPN) with extended delta bar delta (EDBD) algorithm is incorporated into our decision support classifier system. This classifier renders two crucial contributions: (1) the EDBD algorithm accelerates the convergence speed of the learning process and (2) the relative importance (RI) on each band of ancillary information is evaluated rationally.

Life cycle analysis and sewer solids

The search for sustainable ways of living has necessitated a new look at the way in which water services are provided. The new paradigm includes whole-system perspectives for each of the primary criteria groups: social, environmental and economic. Whilst Life Cycle Analysis (LCA) techniques have been used successfully for products, they are much less used to assess processes. Nonetheless there is much to learn from the use of LCA for a much wider range of applications. An application is described whereby LCA has been used to determine energy, mass flows and environmental impacts for a number of sewer-related options for handling sewer solids, using the SimaPro software. This work has been part of a wider study to provide enhanced decision support systems for water utilities.