Intelligent Project Approval Cycle for          Local Government  Case-Based Reasoning Approach            M Kashif Farooq...
ICEGOV09 Partners
Abstract∗ Intelligent workflow for multilevel project approval  cycle∗ Application of CBR for approval of small projects i...
Application Domain∗ CCB   ∗ Citizen Community Board –∗ Small CSO   ∗ Civil Service Organization at local level∗ CCB propos...
Application Domain∗ Social Welfare Department (SWD) of local  government receives these project proposals from  local CCB∗...
Scope of the Paper∗ To provide support and automate the technical  evaluation of the project by using CBR (Case Based  Rea...
Evaluation Parameters∗ Evaluation parameters may be grouped into the two  clusters:  ∗ Class A: Objective Parameters    ∗ ...
Class A: Objective Parameters∗ Nature of Project∗ Budget∗ Profile of CCB or CSO∗ Experience of CCB or CSO∗ Cost of Service
Class B: Subjective Parameters∗ Need of Project∗ Socially Viable∗ Socio-economics∗ Political Support∗ Sustainability∗ Qual...
CBR Based Proposed Approach for        Project Appoval
Case Preparation∗ Collection of n parameters as ith case of the case-base  in the form of a vector as given in equation:  ...
Case Retrieval and Reuse∗ There are many case retrieval methods to match  the current case and a case in the case-base.∗ S...
Manhattan or City distance∗ Our empirical study suggests that Manhattan distance is the most  suitable similarity measure ...
Implementation∗ In first phase we implemented it on the projects  related to the development of small health care units.∗ ...
Table 1. Sample Case Data    #                             Parameters                               Health Care Projects  ...
4     Technically Viable (TV)                                       3.2   3     3.5   3                                   ...
RESULTS∗ Seven macro parameters have been used to define  project evaluation∗ One parameter Predicted Probability to predi...
RESULTS∗ Leave-One-Out (LOO) has been applied as the cross  validation method on a dataset of 50 projects∗ All of the data...
RESULTS∗ We used three evaluation metrics to validate our  results                                       Metric      Resul...
CONCLUSION AND FUTURE WORK∗ Our proposed approach is expected to  provide benefits such as    ∗   quick and efficient deci...
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Intelligent project approval cycle for local government

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In e-government, decision makers need support in their decision processes that may vary from simple nature to complex one. Authorities desire an intelligent workflow for their multilevel approval cycle. In this paper, we propose to use Case Base Reasoning (CBR) for the approval of small projects in public sector. CBR is an artificial intelligence technique which efficiently exploits the past experience to find solution of new problems. The CBR engine maintains a repository of past cases. On a new project approval request, the proposed inference system matches similar historical cases and suggests a solution for the new project. The proposed methodology has been evaluated on a case-base of sample projects.

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Intelligent project approval cycle for local government

  1. 1. Intelligent Project Approval Cycle for Local Government Case-Based Reasoning Approach M Kashif Farooq Malik Jahan Khan Shafay Shamail Mian M Awais
  2. 2. ICEGOV09 Partners
  3. 3. Abstract∗ Intelligent workflow for multilevel project approval cycle∗ Application of CBR for approval of small projects in public sector∗ The CBR engine maintains a repository of past cases∗ On a new project approval request, the proposed inference system matches similar historical cases∗ Suggests a solution for the new project
  4. 4. Application Domain∗ CCB ∗ Citizen Community Board –∗ Small CSO ∗ Civil Service Organization at local level∗ CCB proposes small local level projects related to social development or public service delivery∗ Projects may be ∗ small schools, ∗ health units, ∗ drinking water units, ∗ Roads and streets ∗ parks, ∗ vocational training centers, ∗ advocacy movements for society (public awareness)
  5. 5. Application Domain∗ Social Welfare Department (SWD) of local government receives these project proposals from local CCB∗ CCB has to fund raising up to 20% of total project cost to show public interest∗ If SWD approve the project, then SWD grants 80% amount of total cost of project
  6. 6. Scope of the Paper∗ To provide support and automate the technical evaluation of the project by using CBR (Case Based Reasoning)
  7. 7. Evaluation Parameters∗ Evaluation parameters may be grouped into the two clusters: ∗ Class A: Objective Parameters ∗ Can be evaluated by formula, rule or principle ∗ Class B: Subjective Parameters ∗ Can be assessed by experience
  8. 8. Class A: Objective Parameters∗ Nature of Project∗ Budget∗ Profile of CCB or CSO∗ Experience of CCB or CSO∗ Cost of Service
  9. 9. Class B: Subjective Parameters∗ Need of Project∗ Socially Viable∗ Socio-economics∗ Political Support∗ Sustainability∗ Quality of Service
  10. 10. CBR Based Proposed Approach for Project Appoval
  11. 11. Case Preparation∗ Collection of n parameters as ith case of the case-base in the form of a vector as given in equation: C i = ∑ Pij = ( Pi1 , Pi 2 ,....Pin ).. j∗ C represents a case and each parameter Pij represents defined parameters from the project approval dataset.∗ A case base CB containing m cases may be represented as given in equation: CB = ∑ C k = (C1 , C 2 ...., C m ) k
  12. 12. Case Retrieval and Reuse∗ There are many case retrieval methods to match the current case and a case in the case-base.∗ Some well known methods are ∗ Manhattan distance, ∗ Euclidean distance, ∗ Mahalanobis distance, ∗ Geometric similarity measures, and ∗ Probabilistic similarity measures
  13. 13. Manhattan or City distance∗ Our empirical study suggests that Manhattan distance is the most suitable similarity measure for the domain of project approval cycle as our selected parameters to represent the relevant cases are of numeric nature.∗ It is used to retrieve matching cases from the case-base.∗ It calculates the weighted sum of absolute differences between the current case and any other case in the case-base.∗ This weight is set by the user or analyst. It is given by as d ij = ∑ Wk xik − c jk k ∗ Where dij means distance between ith and jth cases with respect to all parameters ∗ W represents weight. x is the current case while c is the historical case from CB
  14. 14. Implementation∗ In first phase we implemented it on the projects related to the development of small health care units.∗ Experts finalized few critical parameters for the approval of projects.∗ We studied 50 cases and created a case library
  15. 15. Table 1. Sample Case Data # Parameters Health Care Projects P1 P2 P3 P41 Need of Project (NP) 2.5 3 1.5 2.75i Existing facility 3 2 0 2ii Available Alternative 2 3 2 4iii Capacity of existing and alternatives facility 2 4 3 3iv Quality of existing and alternative service 3 3 1 22 Socially Viable (SV) 2.25 2.7 2.5 2.75 5i Cultural conflict 2 3 2 2ii Religious conflict 2 2 3 3iii Awareness and literacy 3 2 3 4iv Negative believes 2 4 2 23 Socio-Economics (SE) 3.3 3 4 3.6i Affordability 3 3 5 4ii Average income per person 4 3 4 4iii Available low cost alternative 3 3 3 3
  16. 16. 4 Technically Viable (TV) 3.2 3 3.5 3 5i Availability of trained staff 3 3 2 3ii Sustainability of trained staff 4 3 4 3iii Availability of utilities (energy, supplies, communication, 3 2 4 2 etc.)iv Technical support for equipment 3 4 4 45 Political Support (PS) 2 3 3.3 3i Political ownership 3 3 4 2ii Political stability 2 4 3 4iii Political conflicts 1 2 3 36 Sustainability (S) 3 3.6 4 3i Financial sustainability 3 4 3 3ii Legal sustainability 2 3 4 3iii Institutionalization 4 4 5 37 Quality of Service (QS) 3 3.3 3 3i Customer or citizen satisfaction 2 4 3 3ii By social audit 4 4 3 2iii By media trial 3 2 3 4
  17. 17. RESULTS∗ Seven macro parameters have been used to define project evaluation∗ One parameter Predicted Probability to predict the matching solution∗ Ci = (NP, SV, SE, TV, PS, S, QS, PP) ∗ PS: Political Support ∗ NP: Need of Project ∗ S: Sustainability ∗ SV: Socially Viable ∗ QS: Quality of Service ∗ SE: Socio-Economics ∗ PP: Predicted Probability of project acceptance ∗ TV: Technically Viable
  18. 18. RESULTS∗ Leave-One-Out (LOO) has been applied as the cross validation method on a dataset of 50 projects∗ All of the data items were labeled, so it was supervised learning process∗ We used solution of one nearest neighbor for reuse phase∗ We adapted simplest revision mechanism which is suggested to pick the second nearest neighbor if the first one does not fit in
  19. 19. RESULTS∗ We used three evaluation metrics to validate our results Metric Result∗ 50 iterations ? ∗ computed accuracy, Accuracy 90% ∗ root mean squared error AAE 0.011 (RMSE) and ∗ average absolute error (AAE) RMSE 0.0376
  20. 20. CONCLUSION AND FUTURE WORK∗ Our proposed approach is expected to provide benefits such as ∗ quick and efficient decision making ∗ process with impartial, ∗ high quality and ∗ informed decisions∗ Current work involved pre-processing of data and did not deal with ambiguous input parameters, it would be very useful to deal with ambiguity and vagueness of the real data in future work

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