Decision Support System Model Research Articles

Bio-inspired voice evaluation mechanism

Decision support systems become a very important part of our lives. Technologies make them applicable almost everywhere. These systems can simplify solutions to the numerous problems, speed up analysis of the results of medical research and contribute to the rapid classification of input patterns. Practical applications may not only contribute to efficient technology, but also improve important aspects of our lives. In this article, we propose a model of decision support system for speech processing. Proposed mechanism can be used in various applications, where voice sample can be evaluated by the use of the proposed methodology. The proposed solution is based on analysis of the speech signal through the developed intelligent technique in which the signal is processed by the composed mathematical transform cooperating with bio-inspired algorithm and spiking neural network to evaluate possible voice problems. A novelty of our idea is the approach to the topic from a different side, because graphical representations of audio signals and heuristic methods are composed for feature extraction. The results are discussed after extensive comparisons in terms of advantages and disadvantages of the proposed approach. As a part of the conducted research, we demonstrated which transformations and heuristic algorithms work better in the process of voice analysis.

Modeling on clinical group decision support system for screening and working diagnosis acute respiratory infections

There are cases of patients with Acute Respiratory Infection (ARI) which are not handled quickly and appropriately at emergency department (ED), thus there should be a medium that can assist medical personnel in making decisions. This research proposes the development model of Clinical Group Decision Support Systems (CGDSS) named KLISPA models. KLISPA model can accommodate a screening process to the working diagnosis process of ARI by integrating Case Based Reasoning (CBR) and Groups Decision Support System (GDSS). Screening process completed by CBR using Nearest Neighbor method. This process helps medical personnel such as nurses, co asst doctors, and general practitioners to do screening to patients for determining the early solution to the disease. Meanwhile, the working diagnosis solved by GDSS uses the methods of Eckenrode, Extended TOPSIS and BoostVote. This process accommodates the needs of joint decision-making which involves groups of Decision Maker (DM) consisting of pediatrician, child lung specialist doctor, and the radiologist doctor.

Group Decision Support System to Determine Regional Development Priority Using the Item-Based Clustering Hybrid Method

Determination of regional development priorities is usually influenced by two factors, firstly the policy-making subjectivity (regional head) second to the results of the development data analysis. But both often do not go hand in hand because in fact subjectivity is more dominant than combining the two. The development of computational technology, especially in the area of decision support, provides space to solve various problems related to the decision making process. This study aims to develop a Group Decision Support System (GDSS) model for determining regional development priorities in the future. An alternative approach in the form of developing a GDSS model is carried out so that it can be used to determine regional development priorities that are able to combine analysis of data on development results and subjectivity of policy makers. It means, policy-enforcing subjectivity still exists but the results of development data analysis are also used. This model combines the GDSS concept, MVHAC cluster technique and Item-based Cluster Hybrid Method (ICHM) to accommodate joint decision making from each group decision maker. The results of the testing of the model developed against six regions in Banten Province show that there are three regions that must be prioritized in the future. Those are Pandeglang, Lebak and Kota Serang.

Breast Cancer Prognosis Using a Machine Learning Approach.

Machine learning (ML) has been recently introduced to develop prognostic classification models that can be used to predict outcomes in individual cancer patients. Here, we report the significance of an ML-based decision support system (DSS), combined with random optimization (RO), to extract prognostic information from routinely collected demographic, clinical and biochemical data of breast cancer (BC) patients. A DSS model was developed in a training set (n = 318), whose performance analysis in the testing set (n = 136) resulted in a C-index for progression-free survival of 0.84, with an accuracy of 86%. Furthermore, the model was capable of stratifying the testing set into two groups of patients with low- or high-risk of progression with a hazard ratio (HR) of 10.9 (p < 0.0001). Validation in multicenter prospective studies and appropriate management of privacy issues in relation to digital electronic health records (EHR) data are presently needed. Nonetheless, we may conclude that the implementation of ML algorithms and RO models into EHR data might help to achieve prognostic information, and has the potential to revolutionize the practice of personalized medicine.

Arguing with Behavior Influence: A Model for Web-Based Group Decision Support Systems

In this work, we propose an argumentation-based dialogue model designed for Web-based Group Decision Support Systems, that considers the decision-makers’ intentions. The intentions are modeled as behavior styles which allow agents to interact with each other as humans would in face-to-face meetings. In addition, we propose a set of arguments that can be used by the agents to perform and evaluate requests, while considering the agents’ behavior style. The inclusion of decision-makers’ intentions intends to create a more reliable and realistic process. Our model proved, in different contexts, that higher levels of consensus and satisfaction are achieved when using agents modeled with behavior styles compared to agents without any features to represent the decision-makers’ intentions.

Method of applying two-step variational-gradient method to dynamic models in decision support systems

Method of applying two-step variational-gradient method to dynamic models in decision support systems

Pairing clients and psychologists using stable marriage problem approach

Health is an important thing in human life, it includes not only physical health, but also mental health. Choosing the right psychologist will help individuals with mental problem to overcome the problem. Choosing a psychologist is important, because the psychologist’s personality will influence the process of psychotherapy. This research paper will describe our approach in building a Decision Support System model that will compute the pairing of clients and psychologists. In this study, Weighted Sum Model is adopted to calculate the preference value of each individual, both psychologists and clients. Then based on this preference value, the client-psychologist pairing will be calculated using the Stable Marriage Problem approach. The Gale-Shapley Algorithm is used in this system to solve the Stable Marriage Problem and has been able to produce a stable matching between one set of Clients and one set of Psychologists, and there is no blocking pair from the results of this algorithm.

Sistem Pendukung Keputusan Kelompok Untuk Menentukan Penerima AGC Award Menggunakan Metode Simple Additive Weighting dan Borda

PT. Astra International continues to develop into one of the leading group of companies in Indonesia. Currenty its manages six core business namely, automotive, financial, services, heavy equipment, mining, agro-business, infrastructure, logistics, and information techonology. In its operations can bring various impact on enviroment quality, such as the avalaibility of natural resources (water, energy and materials), waste generated in the form of solid, fluid, and air. Therefore ESR Division carry out an assessment of the subsidiary programs to minimize the impact. In this research using SAW method to support the decision making of individual decision, and Borda method to support the group decision making and to determine AGC Award recipient. The decision support system model proposed in this study has proven to solve problems that require individual or group decision-making processes.

Model Bantuan Rumah Tidak Layak Huni Menggunakan SPK Desa Cangkringrembang Demak

The need for clean livable houses complete with adequate infrastructure is the right of every citizen. The state in this case the government has clearly set it up in Law No. 6 of 2014 or known as the Village Law and Regents Regulation No.5 of 2016 concerning the allocation of village funds. In this law the spirit of the community to build a better village is not an impossible thing to achieve, moreover financial assistance from the center to the regions is given more than activities for other institutions. But the fact remains on the ground that many residents, especially in the villages, still do not have a place to live that can be said to be livable (prosperous). In this study a Sistem Pendukung Keputusan (SPK) model was built to determine the provision of assistance to villagers. The object of the study was conducted in the Cangkring Rembang Village, Karanganyar District, Demak Regency. The results of this study are a decision support system model with the Multifactor Evaluation Process (MFEP) method to determine the assistance for restoration of unfit homes according to criteria for home ownership, home status, home wall status, home floor status, home roof status, building area status and income status

Modeling Decision Support System for Optimal Disease Diagnosis and Treatment of Cerebral Aneurysm

This paper presents a novel decision support system (DSS) to assist medics administer optimal clinical diagnosis and effective healthcare post-treatment solutions. The DSS model that evolved from the research work predicted treatment of cerebral aneurysm using fuzzy classifications and neural network algorithms specific to patient clinical case data. The Lyapunov stability implemented with Levenberg–Marquardt model was used to advance DSS learning functional paradigms and algorithms in disease diagnosis to mimic specific patient disease conditions and symptoms. Thus, the patients' disease conditions were assigned fuzzy class dummy data to validate the DSS as a functional system in conformity with core sector standards of International Electrotechnical Commission—IEC61508. The disease conditions and symptoms inputted in the DSS simulated synaptic weights assigned linguistic variables defined as likely, unlikely, and very unlikely to represent clinical conditions to specific patient disease states. Furthermore, DSS simulation results correlated with clinical data to predict quantitative coil embolization packing densities required to limit aneurismal inflow, pressure residence time, and flow rate critical to design treatments required. The profiles of blood flow, hazards risks, safety thresholds, and coiling density requirements to reduce aneurismal inflow significantly at lower parent vessel flow rates was predicted by DSS and relates to specific anatomical and physiological parameters for post-treatment of cerebral aneurysm disease.

The Impact of Perioperative Data Science in Hospital Knowledge Management.

Conservative practices, such as manual registry have limited scope regarding preoperative, intraoperative and postoperative decision making, knowledge discovery, analytical techniques and knowledge integration into patient care. To maximize quality and value, perioperative care is changing through new technological developments. In this context, knowledge management practices will enable future transformation and enhancements in healthcare services. By performing a data science and knowledge management research in the perioperative department at Hospital Dr. Nélio Mendonça between 2013 and 2015, this paper describes its principal results. This study showed perioperative decision-making improvement by integrating data science tools on the perioperative electronic system (PES). Before the PES implementation only 1,2% of the nurses registered the preoperative visit and after 87,6% registered it. Regarding the patient features it was possible to assess anxiety and pain levels. A future conceptual model for perioperative decision support systems grounded on data science should be considered as a knowledge management tool.

Strategic decision support systems for short supply chain development in the agrifood sector

There has been an interest in short food supply chain recently which has stemmed from the need of improving the flow both of products and information from suppliers to customers. Although, many tools have been developed in order to increase the performance of supply chain, they haven't succeeded in helping managers make strategic decisions concerning the operations of the short food supply one. In the agrifood sector, practitioners face short food supply chains as integrated systems that incorporate all the processes of traditional supply chains. Thus, decision support systems (DSSs) are required to help managers handle these processes properly. Most of the existing studies focus on the improvement of individual firms or processes more than on the design of an entire food supply chain. So, the purpose of this paper is to propose such a strategic DSS model that based on the Strategic information systems planning (SISP) process, could provide a holistic approach to effective decision making in short supply chain in the agrifood industry. This model supports product managers to improve the effectiveness of food supply operations.

Strategic decision support systems for short supply chain development in the agrifood sector

There has been an interest in short food supply chain recently which has stemmed from the need of improving the flow both of products and information from suppliers to customers. Although, many tools have been developed in order to increase the performance of supply chain, they haven't succeeded in helping managers make strategic decisions concerning the operations of the short food supply one. In the agrifood sector, practitioners face short food supply chains as integrated systems that incorporate all the processes of traditional supply chains. Thus, decision support systems (DSSs) are required to help managers handle these processes properly. Most of the existing studies focus on the improvement of individual firms or processes more than on the design of an entire food supply chain. So, the purpose of this paper is to propose such a strategic DSS model that based on the Strategic information systems planning (SISP) process, could provide a holistic approach to effective decision making in short supply chain in the agrifood industry. This model supports product managers to improve the effectiveness of food supply operations.

MODEL OF MUTUALLY DEPENDENT TRANSPORT FLOWS TRAFIC CONTROL ON INDUSTRIAL RAILWAY TRANSPORT

Issues related to the optimal control of interdependent traffic flows during the dispatching centralization of industrial railway transport facilities are investigated. The problem of the intellectual and analytical support of dispatch control and monitoring systems in industrial transport is considered. Mathematical models have been developed for the optimal control of transport processes on industrial rail transport according to the criteria of maximizing the transported mass of goods and minimizing rolling mileage. Simulation modeling of the intellectual-analytical decision support system was carried out by the operational personnel of the centralized control room, on the basis of which a number of graphical dependencies of optimization parameters were obtained. A program implementation of the developed models with the purpose of their integration with dispatch centralization systems for the formation of recommendation actions to operational personnel is proposed. Identified ways to further research in the subject area. The adequacy of the proposed methods and models is confirmed by simulation. In particular, on the basis of modeling, graphical dependencies of the parameters of cargo flows on the coordinates of the rolling stock and the density of the commodity mass on the transport network of an industrial combine were obtained. The resulting graphical dependencies in their software implementation allow you to directly determine the best ways to manage the plant’s traffic flows. The practical implementation of the methods, models and approaches proposed in the article consists in developing dispatching control and monitoring systems for industrial railway transport on their application software, which will provide recommendations on the optimal management of interdependent train traffic for the transportation of technological raw materials and goods.

Prerequisites for the formation of a safe information-educational environment of a modern university

The paper reviewed and analyzed previous research in the field of protection of the information-educational environment of universities (ILE). It is shown that the priority development of digital education systems in many industrialized countries of the world requires appropriate technical and methodological support of specialists not only in the field of pedagogical activity, but also information technology, taking into account the problems of cyber security and information protection. It is shown that the protected data that is stored and circulate in the information and communication systems of universities, in particular, include: personal data of students, teachers, researchers, support staff; digitized information representing the intellectual property of the educational institution; information arrays that provide the learning process (for example, multimedia content, databases, training programs, etc.); It is substantiated that these information resources can act as an object of theft or distortion from external (internal) computer intruders or from hooliganism, from students or employees. It has been substantiated that the trend towards globalization of access to information resources, formed in many countries, makes relevant the task of introducing the latest digital and information and communication technologies in all areas of activity of a modern university. The relevance of research in the direction of developing models for decision support systems for finding investment management strategies for various ratios of the parameters of the investment process in the cybersecurity systems of educational institutions is substantiated.

An expert decision support system for sandstone acidizing design

Based on the fundamentals of expert system (ES) and decision support system (DSS), we developed an integrated system, the expert decision support system (EDSS), to design and optimize sandstone acidizing. The new system combines knowledge of the ES with DSS models to facilitate decision-making for qualitatively and quantitatively acidizing sandstone reservoirs; this approach greatly strengthens the system’s working capability and widens its applicable range. This article introduces the design principle, system structure, functional modules, multi bases, and development process of the EDSS. We illustrate the representation method of the expert’s knowledge, establishing the knowledge decision tree, and creating quantitative mathematic models for decision support and inference process via different reasonings (rule-based vs. case-based). These methods and methodologies worked together to operate different functional modules for inference from both knowledge and calculation. The EDSS connected different design considerations of the acidizing technology. A field test case study proved that the proposal provided by the EDSS was very efficient.

Decision Support System Model of Teacher Recruitment Using Algorithm C4.5 and Fuzzy Tahani

During the new academic years, schools accept new students either at the level of elementary school (SD), junior high school (SMP), senior or vocational high school (SMA/SMK), as well as the existence of Islamic Education Teachers. Data taken from Local Office of Education (Diknas) of Magelang collaborated with Local Office of Religious Affairs (Kankemenag) of Magelang in 2015-2017, the Islamic Education Teachers (GPAI) recorded in 2015-2017 still less of 275 people of GPAI. It was caused by retirement, prolonged ill, and passed away. Diknas and Kemenag collaboration in openning the recruitment of GPAI is currently still using the manual system (Microsoft Excel), so it causes the administration data in that recruitment less objective and unaccountable, these are the obstacles in the teacher candidates’ recruitment which must be solved soon. How to make a system solves this problem. Meanwhile, the interest of Islamic Education Teacher (GPAI) candidates is high. To support and overcome this issue, an admission system of Islamic Education Teacher (GPAI) candidates is made so the results are accountable. Decision support system of teacher recruitment using algorithm C4.5 and Fuzzy Tahani is a system which can overcome this problem. Researchers try to build a system with the combination between the methods of Algorithm C4.5 and Fuzzy Tahani that can help the decision making in the recruitment of Islamic Education Teacher candidates. From the training results of 484 recorded criteria using attributes (1) Last Diploma; (2) University Accreditation; (3) The Faculty of Education; (4) Islamic Education (PAI) Department; (5) Police Certificate (SKCK); (6) Passport Photo; (7) ID Card; resulting in 290 recorded data of the Entropy and Gain calculation with an average time 3 minutes 15 seconds shows that percentage value of the final result those who cannot be recommended are 33%, then those who receive recommendation are 68%, with the value of entropy produced is 0.591672779.

Usability aspects of medication-related decision support systems in the outpatient setting: A systematic literature review

In this study, we evaluated the usability aspects of medication-related clinical decision support systems in the outpatient setting. Articles published between 2000 and 2016 in Scopus, PubMed and EMBASE were searched and classified into three usability aspects: Effectiveness, Efficiency and Satisfaction. Using Van Welie et al.'s usability model, we categorized usability aspects in terms of usage indicators and means. Out of the 1999 articles, 24 articles met the selection criteria of which the main focus was on reducing inappropriate medication, prescription rate and prescription errors. Evidence could mainly be found for Effectiveness and showed high rates of positive results in reducing medication errors. To date, the effects of Efficiency and Satisfaction of clinical decision support systems regarding medication prescription remain understudied. Usability aspects such as memorability, learnability, adaptability, shortcuts and consistency require more attention. Studies are needed for better insight into the user model and to design a knowledge/task model for clinical decision support systems regarding medication prescription.

Using Web-GIS technology as a smart tool for resiliency management to monitor wind farms performances (Ganjeh site, Iran)

Considering the wide spread locations of wind farms in Iran, it is important to develop a suitable decision support system (DSS) to fulfill proper management of wind farms. Extensive literature survey indicates that there are no integrated forms of DSS to manage a set of wind farms. The existing wind farms are performing independently, and there is no practical method for exchanging the online data. DSS can contribute to optimal operation of wind farms, operation and maintenance scheduling, pricing policy, etc. In this study, a geographic information system and RETSCREEN software were linked to the designed DSS to achieve a more suitable result. Also, a huge number of data are constantly collected and fitted into the DSS to predict future performance of the wind farms, support the decision for pricing policy, and develop next generation of wind farms in the studied area. The developed software, called Monitoring Wind Turbines Performance, was applied to a wind farm located in Ganjeh at the northern part of Iran to ensure the capability of the DSS model. The results show that application of this model can lead to both minimal costs imposed to the wind farm owners and considerable margin of economic gain. Setting up such a network and smart technologies can be considered as a useful tool for energy resiliency management in mega cities at the national level.

The qualitative risk assessment model of riskote decision support system

Sendai Framework for Disaster Risk Reduction 2015-2030 identify among other principles that disaster risk reduction requires a multi-hazard approach and inclusive risk-informed decision-making. The development of a harmonized decision support tool to ensure that planning decisions are informed about the hazards affecting a given territory, the vulnerability of different land uses, exposed elements and options available to mitigate risks is crucial in the context of spatial planning. The objective of this paper is to describe the qualitative risk assessment model used in the development of the RiskOTe decision support system (DSS). RiskOTe is a spatial decision support system to assist spatial planning integrating the risk management component at municipal level. The qualitative risk assessment model used in RiskOTe is based on risk matrix that relates consequences and likelihood. Oeiras municipality was used as case study to demonstrate the applicability of the spatial decision support system. The scenarios obtained from the case study show that decision-making integrating risk analysis can be supported on a solid basis of information obtained from the system.