Tahseen Ahmed Jilani

Multivariate stochastic fuzzy forecasting models

In this paper, we have presented two new multivariate fuzzy time series forecasting methods. These methods assume m-factors with one main factor of interest. Stochastic fuzzy dependence of order k is assumed to define general methods of multivariate fuzzy time series forecasting and control. These new methods are applied for forecasting total number of car road accidents casualties in Belgium using four secondary factors. Practically, in most of the situations, actuaries are interested in analysis of the patterns of casualties in road accidents. Such type of analysis supports in deciding approximate risk classification and forecasting for each area of a city. This directly affects the underwriting process and adjustment of insurance premium, based on risk intensity for each area. National Institute of Statistics, Belgium provides risk intensity based classification of each city. Thus, this work provides support in deciding the appropriate risk associated with an insured in a particular area.

M-Factor High Order Fuzzy Time Series Forecasting for Road Accident Data

In this paper, we have presented new multivariate fuzzy time series (FTS) forecasting method. This method assume m-factors with one main factor of interest. Stochastic fuzzy dependence of order k is assumed to define general method of multivariate FTS forecasting and control. This new method is applied for forecasting total number of car road accidents causalities in Belgium using four secondary factors. Practically, in most of the situations, actuaries are interested in analysis of the patterns of casualties in road accidents. Such type of analysis supports in deciding approximate risk classification and forecasting for each area of a city. This directly affects the underwriting process and adjustment of insurance premium, based on risk intensity for each area. Thus, this work provides support in deciding the appropriate risk associated with an insured in a particular area. Finally, comparison is also made with most recent available work on fuzzy time series forecasting.

PCA-ANN for Classification of Hepatitis-C Patients

In this paper, an automatic diagnosis system based on Neural Network for hepatitis virus is introduced. This automatic diagnosis system deals with the mixture of feature extraction and classification. The system has two stages, which are feature extraction – reduction and classification stages. In the feature extraction – diminution stage, the hepatitis features were obtained from UCI Repository of Machine Learning Databases. Missing values of the instances are adjusted using local mean method. Then, the number of these features was reduced to 6 from 19 due to relative significance of fields. In the classification stage, these reduced features are given as inputs Neural Network classifier. The classification accuracy of this ANN diagnosis system for the diagnosis of hepatitis virus was obtained, this accuracy was around 99.1% for training data and 100% for testing data. General Terms Data Mining

Approximate Knowledge Extraction using MRA for TYPE-I Fuzzy Neural Networks

Using neural network, we try to model the unknown function/for given input-output data pairs. The connection strength of each neuron is updated through learning. Repeated simulations of crisp neural network produce different values of weight factors that are directly affected by the change of different parameters. We propose the idea that for each neuron in the network, we can obtain reduced model with higher efficiency using wavelet based multi-resolution analysis (MRA) to form wavelet based quasi fuzzy weight sets (WBQFWS) through repeated simulation of the crisp neural network. Such type of WBQFWS provides good initial solution for training type-I fuzzified neural networks. As real data is subjected to noise and uncertainty, therefore, WBQWFS help in the simplification of the structure of the complex problems using low dimensional data sets. Such fuzzy sets are also supportive in approximating the sum of knowledge that a hidden or output neuron contains in the learning frameworks

A Two Phase Algorithm for Fuzzy Time Series Forecasting using Genetic Algorithm and Particle Swarm Optimization Techniques

ABSTRACT Fuzzy Time series is being used for forecasting since last two decades for forecasting. Nature inspired computing techniques like other domains are now being used for optimization purpose in Fuzzy Time Series forecasting models to get improved results. In this paper we have presented a new algorithm for multivariate fuzzy time series forecasting having two phases. Genetic Algorithm and Particle Swarm Optimization techniques are used in this algorithm for optimization. We applied our algorithm on Taiwan forex Exchange (TAIFEX) index and got better results and minimized error rate as compared to previous methods. General Terms Nature inspired computing; Time Series forecasting Keywords Fuzzy time series; two-factor high-order fuzzy logical relationships; Genetic Algorithm, Particle Swarm Optimization; TAIFEX index. 1. INTRODUCTION Forecasting is prediction of unseen values of some sequence. It holds significance in economic and financial modeling as entrepreneurs use predicted values for business planning and taking key decisions. In the last two decades Fuzzy Time Series (FTS) is being used for forecasting purpose. Song and Chissom [1, 2] used the concept of FTS and forecasted number of enrollments of the University of Alabama. Many other researchers proposed their models for enrollments forecasting of the University of Alabama ([3], [4], [5], [6], [7], [8], [9], [10] and [11]), temperature prediction [12, 13] and car road accidents [14, 15] using fuzzy time series. Huarng [7] presented heuristic based model for fuzzy time series forecasting. Jilani and Burney [14] presented M-factor high order fuzzy time series forecasting model for car road accident data. Jilani and Burney [16] presented new heuristic based approach for frequency density based partitioning for fuzzy time series forecasting of stock market. In the recent years many researchers started applying Nature inspired computation (NIC) techniques for optimization purpose in FTS forecasting. Kuo et al. [17] and Huang et al.[18] proposed Particle Swarm Optimization (PSO) based FTS forecasting models for enrollments data of the University of Alabama. Park et al. [19] proposed a forecasting model for TAIFEX and KOSPI-200 index, which was based on FTS and swarm intelligence. Jilani et. al. [20] proposed a PSO based FTS forecasting model for car road accidents. Jilani et. al. presented a hybrid algorithm based on Genetic Algorithm (GA) and PSO for forecasting TAIFEX Genes are randomly generated initially to make chromosomes and KSE-100 index. Jilani et. al. [22] presented a trend based heuristic approach using GA for forecasting car road accidents. In this paper we have applied genetic algorithm in first phase to optimize weights, and in second phase we used those weights and optimized interval length to get best forecasting result.

Traceability management framework for patient data in healthcare environment

Change management or configuration management is becoming necessity for every facet of software system development. Traceability of objects i.e. artifacts or information units becomes core talent for authentic determination of the parametric information over the explicit instance of time. This paper presents the evolving and useful concept of traceability management wrapped in change management paradigm that provides perception for requirement analysis phase. We have analyzed the patient data requirements workflow model with respect to the traceability management process. We have proposed a new approach for better traceability of patient data management model (PDMM) for identification and realization of trace relationships within requirements (IRTRR) in healthcare environment. We have named the model as PDMM-IRTRR framework. The key activities represented in the proposed framework are also highlighted.

New Method of Learning and Knowledge Management in Type-I Fuzzy Neural Networks

A new method for modeling and knowledge extraction at each neuron of a neural network using type-I fuzzy sets is presented. This approach of neuron modeling provides a new technique to adjust the fuzzy neural network (FNN) structure for feasible number of hidden neurons and efficient reduction in computation complexity. Through repeated simulations of a crisp neural network, we propose the idea that for each neuron in the network, we can obtain reduced model with high efficiency using wavelet based multiresolution analysis (MRA) to form wavelet based fuzzy weight sets (WBFWS). Triangular and Gaussian membership functions (MFs) are imposed on wavelet based crisp weight sets to form Wavelet Based Quasi Fuzzy Weight Sets (WBQFWS) and Wavelet Based Gaussian Fuzzy Weight Sets (WBGFWS). Such type of WBFWS provides good initial solution for training in type-I FNNs. Thus the possibility space for each synoptic connection is reduced significantly, resulting in fast and confident learning of FNNs. It is shown that propsed modeling approach hold low computational complexity as compared to existing type-I fuzzy neural network models.

Bayesian Network based Elucidatory Model of Change for Flexible Academic System in Post- Graduate Classes

ABSTRACT The purpose of this study is to apply Bayesian Network on change model that supports increase in number of graduate students in post-graduate classes. Change is the process of shifting from current position to desired position. Change can be brought from two modes i.e. behavioral and structural. Three main constraints that confront decision making by a graduate for getting enrolled in post-graduate studies include (i) Non-Readiness, (ii) Rigidity of Academic System and (iii) Shortage of Resources. Nonetheless, three core variables that counter constraints include (i) Readiness that is parenting Awareness Program and Student Counseling (ii) Flexible Academic System that is parenting Distance Learning Program and Flexible Schedule of Classes and (iii) Provision of Resources that is parenting Scholarship Program and Higher-Education Allowance for Parents. A survey feedback of 121 respondent (graduate and not studying further) from Karachi represents that ―Flexible Academic System‖ is more likely to be considered while making decision for getting admission in post-grad studies. Cumulative causal effect of ―Flexible Academic System‖ to the dependent variable ―Admissions in Post-Grad Programs‖ is 0.4, ―Readiness‖ has 0.19 and ―Provision of Resources‖ has 0.18. Cumulative causal effect of entire model is 0.77 that seems fruitful, if executes. In this context, government and NGOs should work on Readiness and Provision of Resources while universities should offer Flexible Academic System in order to increase number of students in post-grad classes.