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Articles by A. Mustapha
Total Records ( 5 ) for A. Mustapha
  S.A. Ali , N. Sulaiman , A. Mustapha and N. Mustapha
  This study focused on improving the dialogue act classification to classify a user utterance into a response class using a decision tree approach. Decision tree classifier is tested on 64 mixed-initiative, transaction dialogue corpus in theater domain. The result from the comparative experiment show that decision tree able to achieve 81.95% recognition accuracy in classification better than the 73.9% obtained using Bayesian networks and 71.3% achieved by using Maximum likelihood estimation. This result showed that the performance of decision tree as classifier is well suited for these tasks.
  S.A. Ali , N. Sulaiman , A. Mustapha and N. Mustapha
  The use of deep generation with statistical-based surface generation merits from response utterances readily available from corpus. Representation and quality of the instance data are the foremost factors that affect classification accuracy of the statistical-based method. Thus, in classification task, any irrelevant or unreliable tagging of response classes represented will result in low accuracy. This study focused on improving dialogue act classification of a user utterance into a response class by clustering the semantic and pragmatic features extracted from each user utterance. A Decision tree approach is used to classify 64 mixed-initiative, transaction dialogue corpus in theater domain. The experiment shows that by using clustering technique in pre-processing stage for re-tagging response classes, the Decision tree is able to achieve 97.5% recognition accuracy in classification, better than the 81.95% recognition accuracy when using Decision tree alone.
  N.H. Abdul Hamid , A. Ahmad , M.S. Ahmad , Y.C. Alicia Tang and A. Mustapha
  Simulation is a way of doing thought experiments besides the deduction and induction methods. Agent-based Simulation (ABS) falls under the domain of artificial intelligence when agent is used to perform certain tasks such as behaviour prediction, optimization of functions or time-constrained work-flow management. This work discussed the use of ABS on a computational normative framework based on a set of empirical characteristics that influence agents’ performance in time-constrained environment. The ABS simulates a domain called the Examination Preparation and Moderation Process (EPMP) which entails document submission processes with deadlines. The simulation is conducted in six different environments and the results of the agent performance in each environment are presented and discussed. The results indicate that the simulation conducted in the EPMP is suitable and effective for evaluating normative agent-based systems.
  U. Musa , S.S. Hati , Y.I. Adamu and A. Mustapha
  Smoked fish species, Clarias sp., Gymnarchus niloticus and Tilapia sp., sampled from the open markets in North-Eastern Nigeria were investigated for the presence and concentration levels of pesticide residues of DDT, dichlorvos and lindane. The GC-MS and GC-FID techniques were employed in the determination of the pesticide residue. The obtained results showed positive identification of op-DDT (2.844-4.220 μg g-1), pp-DDT (3.821-4.479 μg g-1), dichlorvos (2.844-4.220 μg g-1) and lindane (3.479-9.878 μg g-1). Gymnarchus niloticus showed consistently higher pesticide residue levels in the studied smoked fish samples followed by Clarias sp. and then Tilapia sp.
  P. Appalasamy , A. Mustapha , N.D. Rizal , F. Johari and A.F. Mansor
  Modeling the complex human taste is an important focus in wine industries. The main purpose of this study was to predict wine quality based on physicochemical data. This study was also conducted to identify outlier or anomaly in sample wine set in order to detect adulteration of wine. In this project, two large separate datasets are used, which contains 1, 599 instances for red wine and 4, 989 instances for white wine with 11 attributes of physicochemical data such as alcohol, PH and sulfates. Two classification algorithms, Decision tree and Naïve Bayes are applied on the dataset and the performance of these two algorithms is compared. Results showed that Decision tree (ID3) outperformed Naïve Bayesian techniques particularly in red wine, which is the most common type. The study also showed that two attributes, alcohol and volatile-acidity contribute highly to wine quality. White wine is also more sensitive to changes in physicochemistry as opposed to red wine, hence higher level of handling care is necessary. This research concludes that classification approach will give rooms for corrective measure to be taken in effort to increase the quality of wine during production.
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