Intelligent Automated Small and Medium Enterprise (SME) Loan Application Processing System Using Neuro-CBR Approach

Developing a group of diverse and competitive small and medium enterprises (SMEs) is a central theme towards achieving sustainable economic growth. SMEs are crucial to the economic growth process and play an important role in the country's overall production network. The focus of this study is to develop an automated decision support model for SMEs sector that can be used by the management to accelerate the loan application processing. This study proposed an intelligent automated SME loan application processing system (i-SMEs) that is a web based application system for processing and monitoring SME applications using Hybrid Intelligent technique which integrate Neural Network and Case-based Reasoning namely NeuroCBR. i-SMEs is used to assist SME bank management in order to improve decision making time processing as well as operational cost. i-SMEs be able to classify SME market segment into three distinctive groups that are MICRO, MEDIUM and SMALL and also can make a pre-approval loan processing faster. It is possible to transform the patterns generated from i-SME into actionable plans that are likely to help the SME Bank

Diagnosing Hepatitis Using Hybrid Fuzzy-CBR

The Malaysia populations are currently estimated to be 28.9 million with a number of medical specialists is 2,500 and 20,280 doctors. This ratio figures to cause patients need to wait longer in government hospitals and clinics before they can meet doctor or medical specialist. In order to resolve this problem, Ministry of Health has pledged to reduce waiting time of patient examination from 45 minutes to 30 minutes by provide allocation of large budget to the medical sector. This budget will be used either to buy new equipment, which can work with large capacity or upgrade the old equipment to work faster or build the new hospital to tend more patients or hire other doctors from overseas. Due to that reason and the coming which World Hepatitis Day on 28 July 2012, this study proposes a the use of hybrid intelligent, which combine Fuzzy Logic and Case-Based Reasoning (CBR) approach that could be integrated in the diagnosis system to classify patient condition by using fuzzy technique and similarity measurement based on current symptoms of a hepatitis patient. Focus of this study is to develop an automated decision support system that can be used by the doctors to accelerate diagnosis processing. As a result, a prototype called Intelligent Medical Decision Support System (IMDSS) using Fuzzy-CBR engine for diagnosis purposes has been developed, validated and evaluated in this study. The finding through validation and evaluation phase indicates that IMDSS is reliable in assisting doctors during the diagnosis process. In fact, the diagnosis of a patient has become easier than the manual process and easy to use

An initial state of design and development of intelligent knowledge discovery system for stock exchange database

Data mining is a challenging matter in research field for the last few years.Researchers are using different techniques in data mining.This paper discussed the initial state of Design and Development Intelligent Knowledge Discovery System for Stock Exchange (SE) Databases. We divide our problem in two modules.In first module we define Fuzzy Rule Base System to determined vague information in stock exchange databases.After normalizing massive amount of data we will apply our proposed approach, Mining Frequent Patterns with Neural Networks.Future prediction (e.g., political condition, corporation factors, macro economy factors, and psychological factors of investors) perform an important rule in Stock Exchange, so in our prediction model we will be able to predict results more precisely.In second module we will generate clustering algorithm. Generally our clustering algorithm consists of two steps including training and running steps.The training step is conducted for generating the neural network knowledge based on clustering.In running step, neural network knowledge based is used for supporting the Module in order to generate learned complete data, transformed data and interesting clusters that will help to generate interesting rules

Bid or no bid decision making tool using analytic hierarchy process

2016 Fall.Includes bibliographical references.In today's competitive business environment, every construction company confronts a decision-making dilemma and must decide whether to bid or not bid on a project(s) or which project(s) to bid on among candidates. Even though the decision-makers come to the conclusion with different judgments, a final evaluation always requires putting different factors into consideration and contemplating the ups and downs of a project. Therefore, bid or no bid decision is complex and crucial for construction companies. The complexity comes from the consideration of many intangible and tangible factors in the decision-making process (Mohanty 1992). Decision-making is hard because it requires a decision-maker to construct a structured thinking to include many unknown, yet complex variables and compare them simultaneously. Decision-making is crucial because poorly made bidding decisions could cause severe and irrevocable problems. For example, not bidding a favorable project could result in lost opportunities for companies to make profit, improve contractor's strength in the industry and gain a long-term relationship with a new client. On the other hand, bidding a project that actually does not fit the company's profile requires a lot of time, effort, and commitment without a favorable outcome (Ahmad 1990, Wanous et al. 2003). Given that "competitive bidding" is the most common bidding method in the construction industry among others (e.g., negotiated contracts, package deals, private finance initiative), investigating bidding strategies has been a focal point by researchers (Harris et al. 2006). Furthermore, more than 100 key factors that influence bidding decisions have been determined to date since the mid-1950s. Simultaneously, to expedite the process, numerous decision-making models have been proposed. Despite the excessive availability of the factors and decision-making models, the facilitation rate of the subsidiary tools in the evaluation process in the construction industry is very little. According to a survey by Ahmad & Minkarah (1988), only 11.1 percent of the construction companies use a decision making tool in order to come to a bid or not bid conclusion in the United States. The ultimate purpose of this study is to develop a practical decision-making tool to assist decision makers in the construction industry to select the most appropriate projects to bid on using Analytic Hierarchy Process (AHP). Based on the collected demographic information (e.g., sector, size, type), the combined importance weights of the construction professionals are also presented in the study. Finally, the statistically significant differences between different groups of construction companies in how much weight they assign to a given bid/no bid decision factor is investigated. In reaching the abovementioned purpose, the following questions are addressed: • What are the most common key factors that influence bid/no bid decisions? • How can different judgments from different decision-makers be combined into one final decision? • How differently the construction companies in the United States (US) value the key factors that are commonly utilized to make bid/no bid decisions? The validation of the bid/no bid decision-making tool was performed based on two participants' responses; and the tool provided accurate results for one of the evaluations. Because of insufficient response rate to the validation process, it cannot be concluded that the bid/no bid decision-making tool is validated; however the results of the participants point out the need for further research. The results showed that the compliance with the business plan and location of the project factors were found statistically significantly different for the "Contractor Type" classification. On the contrary, none of the key factors was found statistically significantly different for the "Contractor Sector" groups. For the "Contractor Size" classification, the compliance with the business plan factor was found statistically significantly different. The Group AHP approach allows construction companies to come with a combined bidding judgment instead of using the tool individually. As a major finding of this study is that, the contractors grouped under each construction classifications (i.e., Contractor Type, Contractor Sector and Contractor Size) put more value on the overall firm related-internal factors than the overall project related-external factors based on the Group AHP results. It is also found that the project duration and project size key factors have the lowest weights for all contractor classification groups. This study contributes to the construction engineering and management body of knowledge by providing an user friendly decision-making tool to be used in deciding whether to bid or not bid on a project or which project(s) to bid on and advancing the current state of the knowledge on the different weights/values given to the factors by construction companies with different demographics

ADAPT: Approach to Develop context-Aware solutions for Personalised asthma managemenT

The creation of sensors allowing the collection of a high amount of data has been possible thanks to the evolution of information and communication technology. These data must be properly interpreted to deliver meaningful information and services. Context-aware reasoning plays an important role in this task, and it is considered as a hot topic to study in the development of solutions that can be categorised under the scope of Intelligent Environments. This research work studies the use of context-aware reasoning as a tool to provide support in the asthma management process. The contribution of this study is presented as the Approach to Develop context-Aware solutions for Personalised asthma managemenT (ADAPT), which can be used as a guideline to create solutions supporting asthma management in a personalised way. ADAPT proposes context-aware reasoning as an appropriate tool to achieve the personalisation that is required to address the heterogeneity of asthma. This heterogeneity makes people with asthma have different triggers provoking their exacerbations and to experience different symptoms when their exacerbations occur, which is considered as the most challenging characteristic of the condition when it comes to implementing asthma treatments. ADAPT context dimensions are the main contribution of the research work as they directly address the heterogeneity of asthma management by allowing the development of preventive and reactive features that can be customised depending on the characteristics of a person with asthma. The approach also provides support to people not knowing their triggers properly through case-based reasoning, and includes virtual assistant as a complementing technology supporting asthma management. The comprehensive nature of ADAPT motivates the study of the interaction between context-aware reasoning and case-based reasoning in Intelligent Environments, which is also reported as a key contribution of the research work. The inclusion of people with asthma, carers and experts in respiratory conditions in the experiments of the research project was possible to achieve thanks to the collaboration formed with Asthma UK