Futuristic Decision Research Articles

Application of Hybrid Approach in Banking System: An Undesirable Operational Performance Modelling

Non-performing loans (NPLs) is a critical constituent that impacts the operational performance of banks. Rising level of risk leads to poor operational performance, especially when it is beyond the bank’s capabilities to control the increasing bad assets. This calls for real-time performance assessment coupled with futuristic decision making to support banking managers. This observation motivates the authors of this article to develop a two-stage performance prediction assessment model. Accordingly, a hybrid approach combining data envelopment analysis (DEA) and artificial neural network (ANN) is developed to measure and predict the operational efficiency scores of banks. DEA effectively explores the operational performance as well as improvable areas of inefficient banks. The training of ANN model is dependent on estimated operational DEA efficiency scores with the objective to estimate the efficiency scores. Domain for the validation of this study includes dataset derived from Indian banks. The validation result shows that trained ANN model has the prediction capacity with minimum error and maximum accuracy. Finally, the outcome of this study is significantly directed towards business managers who can rely on predictions based on empirical findings of this proposed hybrid modelling.

Predictive visual analytics of event evolution for user-created context

Digital big data provide the vast potential of increasing effectiveness and efficiency for decision making. Since the volume of the data is enormous, the data analysis requires large amount of time and effort. It is more problematic when predictive analysis is necessary for futuristic decision making. For predictive analysis, there have been many studies to forecast future trends spatio-temporally. However, most of studies provide just future tendency per event using graphs or maps without contextual compositive analysis. In this paper, we present a predictive visual analytics system to provide predictive event patterns. We infer the future event evolution by combining contextually similar cases occurring in the past. We utilize social media data to detect interesting abnormal events and match the detected abnormal events within the past news media data to retrieve similar event patterns. Then, we extract future event patterns through compositing contextual relationship among topics included in the similar past patterns. To evaluate our VA system, we demonstrate three use cases in this paper and validate our system with possible predictive story lines. In addition, we present an informal user study and feedback to validate the effectiveness of our system and improve the system in the future. Graphical Abstract text

EPIDEMIOLOGICAL, CLINICAL AND LABORATORY FEATURES OF ROTAVIRUS GASTROENTERITIS AMONG HOSPITALIZED CHILDREN LESS THAN FIVE YEARS OLD IN SELECTED HOSPITALS IN JORDAN, 2007-2008

Background: Acute gastroenteritis continues to be a significant cause of morbidity and mortality worldwide. Rotavirus causes more than 130 million episodes of severe diarrhea in children less than five years throughout the world and is considered as main problem in many countries. Few data are available about gastroenteritis attributable to rotavirus in Jordan. Objectives: The primary purpose of this study is to determine epidemiological, clinical and laboratory characteristics of hospitalized cases attributable to rotavirus gastroenteritis in children less than five years old in selected hospitals during the period May 2007-April 2008 to provide Ministry of Health with evidence upon which to base its futuristic decision in regard to development of more suitable preventive measures. This study will improve the existing diarrhea surveillance system in MoH, facilitate and support the decision of introducing rotavirus vaccine in national immunization programme. Methods: Descriptive Cross-sectional study was performed on the hospitalized children less than five years old with acute gastroenteritis in three selected hospitals in three governorates (north, middle and south regions) with population about 45.3% of Jordan population for the period May 2007 to April 2008. Standard case definition was adopted. Analysis was performed using SPSS and Epi Info statistical packages. Results: 377 cases were enrolled with 171 positive for rotavirus. Results showed that Incidence rate of rotavirus disease is 5.2/ 10000 children less than five years old per year; male: female ratio is 1.8:1 with largest proportion of cases among children 6-11 months old. Southern region has the highest incidence rate. Vomiting is an outstanding symptom for rotavirus cases (P< 0.01) in addition to diarrhea and fever which require admission to hospital for fluid replacement. Majority of rotavirus cases were with some dehydration (30%). Mean stay duration is 3 days. Predominant genotypes are G1P8.G2P4 and G9P untypeable. Conclusions: Although mortality of gastroenteritis is no longer a problem in Jordan due to accessibility to health services, morbidity is still high and rotavirus is a relevant cause for gastroenteritis in children less than five years old; which require hospitalization most of the time (45.4% of admitted gastroenteritis).

ANP-AHP Supported Group Decision Support System for Public and Private Sector Participation in New Delhi Expressway System

Delhiite‟s love affair with the automobile is becoming even more „passionate‟ and inspite of the authorities trying desperately hard, the traffic gridlock merely moves on to the next intersection. The complexity of the goals of New Delhi Transport System and its relationship with other Public and Private Organizations makes the managerial decision making regarding New Delhi Expressway System Planning, a multicriteria, multi-dimensional and multi-approach task. It is not realistic to submit decisions to such a critical problem without proper visualization of the preferred future. In this paper, a ANP-AHP based Group Decision Support System for Information Visualization is presented and its main components are analyzed and discussed for effective managerial decision making by mutual participation of public and private sector in New Delhi Expressway System to solve the city‟s traffic problems. The Model Base Management Subsystem interacts with Project Planning and Implementation Module, Project Financing and Managing Module, Project Infrastructure Leasing Module, Subsidy and Pricing Module; Multi-criteria Iterative Futuristic Decision Process Methodology based Multi-criteria Futuristic Decision Process Platform and Quality Decision Network-Virtual Desk through Central Vision Navigator Board for strategic, tactical and operational planning of New Delhi Expressway System for the next 5 years.

Decision Priorities and Scenarios for Minimizing Electrical Power Loss in an Indian Power System Network

An electrical distribution system transfers electrical energy received at substations to domestic, industrial, agricultural, and commercial customers. All these customers expect electrical energy to always be available. The power loss in the distribution network, due to technical and nontechnical reasons, causes a reduction in overall performance. In this paper an attempt has been made to develop a Loss Management model, using Multi-Criteria Futuristic Decision-Making Methodology (MCFDM), to identify various technical and nontechnical losses in the electric power distribution sector and select various losses to be minimized on a priority basis. The Loss Management model, a systematic futuristic decision priority-ranking tool, is based on five principles: Future scan, Decomposition, Clustering, Prioritization, and Synthesis. Overall Futuristic Decision Priority Weights were computed using the CRITERIA EXPERT computer program, and scenarios for prioritized Multi-Futuristic Decision Factors were generated.

Integrated Multimedia Based Intelligent Group Decision Support System for Electrical Power Network

Electrical Power Network in recent time requires an intelligent, virtual environment based decision process for the coordination of all its individual elements and the interrelated tasks. Its ultimate goal is to achieve maximum productivity and efficiency through the efficient and effective application of generation, transmission, distribution, pricing and regulatory systems. However, the complexity of electrical power network and the presence of conflicting multiple goals and objectives postulated by various groups emphasized the need of an intelligent group decision support system approach in this field. In this paper, an Integrated Multimedia based Intelligent Group Decision Support System (IM1GDSS) is presented, and its main components are analyzed and discussed. In particular attention is focused on the Data Base, Model Base, Central Black Board (CBB) and Multicriteria Futuristic Decision Process (MFDP) module. The model base interacts with Electrical Power Network Load Forecasting and Planning (EPNLFP) Module; Resource Optimization, Modeling and Simulation (ROMAS) Module; Electrical Power Network Control and Evaluation Process (EPNCAEP) Module, and MFDP Module through CBB for strategic planning, management control, operational planning and transaction processing. The richness of multimedia channels adds a totally new dimension in a group decision making for Electrical Power Network. The proposed IMIGDSS is a user friendly, highly interactive group decision making system, based on efficient intelligent and multimedia communication support for group discussions, retrieval of content and multi criteria decision analysis.