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- PublicationDesign and development a voice-called personal assistant on an autopilot quadcopter(2017)This thesis reports the design and development of a voice-called personal assistant on an autopilot quadcopter. The application developed is a voice-controlled application called "Dino". Like in Iron Man movie, Tony Stark have his own virtual assistant called Jarvis to manage almost everything related to technology in his house including in his Iron Man suit. For this project, Dino was developed to control the function of the Quadcopter using a voice command from the user. When the user gives direction, the Quadcopter can respond to the direction, as well as giving feedback to the user. Besides, the user also can ask any general information or update social networks. This is called a two-way communication application. This project requires two main hardware; one is the Raspberry Pi that run Dino, and another one is the Arduino Pro Mini which connected to the Quadcopter. When the user gives direction, the data is received into the Raspberry Pi, the Dino's engines will retrieve the data and at the same time, transfer it to the microcontroller on-board of the Quadcopter. Since the application enables connection with a Quadcopter, the application needs to be compatible with wireless connections. "Dino" improves the control system of the Quadcopter as well as making the Quadcopter as a virtual assistant for the user to communicate in two-way communications.
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- PublicationRUU CCUS dibentangkan di Kabinet minggu depan(2024-05-17)
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- PublicationImproving SVM-based nontechnical loss detection in power utility using the fuzzy inference system(2011)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.
- PublicationImplementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index(Springer Science and Business Media Deutschland GmbH, 2020)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.
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