Welcome to iRepository UNITEN

iRepository is an institutional research repository for Universiti Tenaga Nasional (UNITEN).

The purpose of an institutional repository system at UNITEN (Universiti Tenaga Nasional) is to establish a digital platform that enables the university to collect, preserve, and share its scholarly and intellectual outputs. This centralized repository serves as a hub where UNITEN can store and make openly accessible various types of content, including research articles, theses, conference papers, datasets, and multimedia materials. By implementing an institutional repository system, UNITEN aims to promote open access to knowledge, facilitate research collaboration, enhance the visibility and impact of its research outputs, and ensure the long-term preservation of the university's intellectual assets.

 
Research outputs
22816
Projects
0
People
3
Recent Additions
Most viewed
  • Publication
    Improving SVM-based nontechnical loss detection in power utility using the fuzzy inference system
    (2011)
    Nagi J.
    ;
    Yap K.S.
    ;
    Tiong S.K.
    ;
    Ahmed S.K.
    ;
    Nagi F.
    ;
    25825455100
    ;
    24448864400
    ;
    15128307800
    ;
    25926812900
    ;
    56272534200
    This letter extends previous research work in modeling a nontechnical loss (NTL) framework for the detection of fraud and electricity theft in power distribution utilities. Previous work was carried out by using a support vector machine (SVM)-based NTL detection framework resulting in a detection hitrate of 60%. This letter presents the inclusion of human knowledge and expertise into the SVM-based fraud detection model (FDM) with the introduction of a fuzzy inference system (FIS), in the form of fuzzy if-then rules. The FIS acts as a postprocessing scheme for short-listing customer suspects with higher probabilities of fraud activities. With the implementation of this improved SVM-FIS computational intelligence FDM, Tenaga Nasional Berhad Distribution's detection hitrate has increased from 60% to 72%, thus proving to be cost effective. � 2011 IEEE.
  • Publication
    Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index
    (Springer Science and Business Media Deutschland GmbH, 2020)
    Abba S.I.
    ;
    Pham Q.B.
    ;
    Saini G.
    ;
    Linh N.T.T.
    ;
    Ahmed A.N.
    ;
    Mohajane M.
    ;
    Khaledian M.
    ;
    Abdulkadir R.A.
    ;
    Bach Q.-V.
    ;
    57208942739
    ;
    57208495034
    ;
    57197592021
    ;
    57211268069
    ;
    57214837520
    ;
    57195618368
    ;
    23089044300
    ;
    57200567560
    ;
    23033338600
    In recent decades, various conventional techniques have been formulated around the world to evaluate the overall water quality (WQ) at particular locations. In the present study, back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and one multilinear regression (MLR) are considered for the prediction of water quality index (WQI) at three stations, namely Nizamuddin, Palla, and Udi (Chambal), across the Yamuna River, India. The nonlinear ensemble technique was proposed using the neural network ensemble (NNE) approach to improve the performance accuracy of the single models. The observed WQ parameters were provided by the Central Pollution Control Board (CPCB) including dissolved oxygen (DO), pH, biological oxygen demand (BOD), ammonia (NH3), temperature (T), and WQI. The performance of the models was evaluated by various statistical indices. The obtained results indicated the feasibility of the developed data intelligence models for predicting the WQI at the three stations with the superior modelling results of the NNE. The results also showed that the minimum values for root mean square�(RMS) varied between 0.1213 and 0.4107, 0.003 and 0.0367, and 0.002 and 0.0272 for Nizamuddin, Palla, and Udi (Chambal), respectively. ANFIS-M3, BPNN-M4, and BPNN-M3 improved the performance with regard to an absolute error by 41%, 4%, and 3%, over other models for Nizamuddin, Palla, and Udi (Chambal) stations, respectively. The predictive comparison demonstrated that NNE proved to be effective and can therefore serve as a reliable prediction approach. The inferences of this paper would be of interest to policymakers in terms of WQ for establishing sustainable management strategies of water resources. � 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
  • Publication
    Mechanics II: Dynamics - CMPF 112 - Trimester 1, 2015/2016
    (2015-09)
    College of Foundation and Diploma Studies
  • Publication
    Business analytics - CISB 474 - Semester 1, 2018/2019
    (2018-09)
    College of Computer Science and Information Technology
  • Publication
    International marketing - MKEB 333 - Semester 2, 2016/2017
    (2017-01-23)
    College of Business Management and Accounting