Scopus ID: 85081580542
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (2019), Vol. XLII-4/W19, pp. 133-140
Adrian Christopher E. Cruz, Jason Matthew D. Dizon, ... John Manuel B. Vergel
Journal Article | Published: December 23, 2019
Abstract
The intensity of urban flooding area due to rapid urbanization in Metro Manila has been worsening over the years caused by the torrential rains brought by the Southwest Monsoon. To further characterize the impact of land use change influenced by urbanization, we compared the flood map generated from two periods (Year 200 & Year 2020) using a two-dimensional hydrodynamic modelling simulated in FLO-2D software. In our simulations, we assigned roughness coefficient values to corresponding land use category derived from an earlier study in the area previously spearhead by JICA in 2001. Each model will incorporate the implemented Year 2000 land use and the projected Year 2010 land use classification respectively, which were used in this earlier study. Meanwhile, both models will use the same sets of parameters for the simulation: IFSAR-derived DEM elevation model and a rainfall event with 10-yr return period. The area of interest of this study is located near Valenzuela-Obando-Meycauayan (VOM) with its boundaries defined from the National Mapping and Resource Information Authority. The flood simulations conducted do not take into consideration in existing flood control measures such as drainage systems and floodwalls to minimize the complexity of the model. The results are evaluated both quantitatively and qualitatively. According to the results, the impact of the land use change on flood formation in most areas are insignificant due to a low degree of land use change. However, there has been substantial impact on flooding in specific areas where there is a major change in the land use. For further studies, we recommend the use of a longer land use change period and the consideration of more varied and precise Manning’s n-values.
Scopus ID: 85080942322
2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), (2019), pp. 608-613
Marie Luvett I. Goh
& Joselito Eduard E. GohConference Paper | Published: December 1, 2019
Abstract
In the Philippines, Light Rail Transit (LRT) 1 is one of the most used public mass rapid transports by Filipino commuters in going to their respective destinations in Metro Manila. However, conditions of the trains have been deteriorating over the past years resulting to insufficient numbers of trains to meet the commuter demands during peak hours causing irate passengers, delays in train arrival and uncomfortably crowded stations and trains. Currently, LRT1 implements Passenger Limit Per Platform (PLPP) to regulate load capacity at the station platforms, prevent overloading of trains and congestion at the paid area. But the said scheme is being done manually which is tedious to staff and is prone to error. Thus, this study presents the integration of embedded system and different software applications to manage the crowd of all LRT1 stations platforms and trains intelligently. A Simulation software was developed to populate data to different stations that are relevant during operations in the absence of the station prototype. Integration and acceptance tests showed that all components of the system are functioning accurately according to the predetermined design specifications. The developed system proves to be functionally acceptable in terms of suitability and accuracy, and highly functional in terms of security. Thus, the overall system is functionally acceptable as perceived by the respondents as manifested by the mean rating of 3.28.
Scopus ID: 85080932573
2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), (2019), pp. 718-723
Conference Paper | Published: December 1, 2019
Abstract
Medicine intake, as prescribed by physicians and health care providers, is important not only for minimizing the risk of relapse but also to treating conditions and improving one’s overall well-being. However, adherence to a medication routine seems to be a problem for some people which is usually affected by a variety of factors such as hectic day-to-day activity schedules, poor prescription instruction, concurrent intake of multiple medications, and forgetfulness. Medication adherence has been then considered as one of the major medical problems globally. In such cases, a medical device that could alert and remind patients in taking their medicines on time will come in handy. Consequently, this study aimed to design and develop a pocket-sized electronic pillbox device using TFT LCD display, Arduino microcontroller, Piezo Buzzer (for sound notification), Eccentric Rotating Mass (for vibration notification), Lithium Ion battery as power source, and plastic organizer as the main body. The said pillbox device will act as a countermeasure for medication non-adherence particularly by patients under the case of polypharmacy. Thus, this study focused on the design and development of the prototype, hardware testing and system qualification only. Furthermore, this paper is part of a future study where the assessment and measure of device behavior and adherence will be conducted to compare whether the utilization of pillbox device has an impact to the people who are using it.
Scopus ID: 85080880148
2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), (2019), pp. 581-586
Conference Paper | Published: December 1, 2019
Abstract
This study is concerned with the development of an information system for disaster risk reduction and management in the Philippines. It covered relational and multi-dimensional database designs as well as software applications for disaster preparedness and response combined with Decision Support System. The application highlights the following administrative modules namely community registration with fingerprint biometrics and camera integration, emergency evacuation, search and rescue operation, cash and in-kind donation, evacuation center and disaster event profiling, weather forecast, and private messaging. Moreover, the decision support system highlights the live data consolidation of disaster affected areas and individuals through data visualization and geographic information system. It presents historical information of previous disasters in a multi-dimensional viewpoint from national level to barangay or district level. Finally, the system can dynamically generate predicted list of potential evacuees via Logistic Regression Analysis. The system's response time test revealed a highly acceptable result with latency ranging from 31ms to 419ms. The software quality evaluation in terms of functionality, usability, efficiency, and maintainability proved to be excellent and is highly commendable by the ICT department and operations group of the National Disaster Risk Reduction and Management Council, Office of Civil Defense.
Scopus ID: 85079283298
Proceedings of the 3rd International Conference on Telecommunications and Communication Engineering, (2019), pp. 51-56
Conference Paper | Published: November 9, 2019
Abstract
In the Philippines, according to Philippine Authority of Statistics, there is an imbalance between the student enrollment and student graduation. Almost half of the first-time freshmen full time students who began seeking a bachelor's degree do not graduate on time. The study aims to utilize how Naïve Bayes algorithm - a data classification algorithm that is based on probabilistic analysis - can be used in educational data mining specifically in student graduation. The study is focused on the application of the Naïve Bayes algorithm in predicting student graduation by generating a model that could early predict and identify students who are prone of not having graduation on time, so proper remediation and retention policies can be formulated and implemented by institutions.
Scopus ID: 85084765618
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-4
Conference Paper | Published: November 1, 2019
Abstract
The use of reinforced concrete as a structural material originates from two different components; Reinforcing steel, known as a highly-tensile material, combined with concrete, a highly-compressive material, act together to resist different types of loadings. However, this composite action is dependent upon the transfer of loads between the steel and concrete known as the bond and is concluded to be on a form of continuous stress along the juncture of steel and concrete. This study focused on producing a bond stress model as a function of concrete compressive strength, embedment length and bar diameter, using single pullout test, with the help of multiple regression analysis by using Microsoft Excel. For the compressive strength, 40 MPa, 50MPa and 60MPa were used, with 50mm, 75mm and 100mm embedment length and bar diameters of 12mm, 16mm and 20mm, producing a total of 27 specimens. After subjecting each specimen to pull out test, data were tabulated and analyzed using Multiple Regression producing a model with 97.81% correlation coefficient. The model was then subjected to parametric testing and it was concluded that increasing the compressive strength of concrete would result to an increase in bond stress; however, increasing the embedment length and bar diameter would result to a decrease in bond stress.
Scopus ID: 85084759812
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-6
Conference Paper | Published: November 1, 2019
Abstract
Pulsars enabled astronomers to study neutron stars and verify general relativity under intense gravitational field conditions. However, finding pulsars is not as easy as it seems because most of them have weak pulses that get drowned in the background noise and hence do not get detected. This paper presents a novel way of classifying radio emission patterns collected from a radio telescope whether it is from a pulsar or not through machine learning and genetic algorithm. The dataset was acquired from the High Time Resolution Universe (HTRU) survey two which contains eight numerical features and one target variable describing the pulse profile. Synthetic Minority Oversampling Technique (SMOTE) was applied to the dataset to fix the imbalance between classes. A genetic algorithm library was used to automatically select the best feature preprocessing method, feature selection/reduction technique, machine learning model inside the scikit-learn library, and hyperparameter settings. The genetic algorithm suggested using a single stack and multiple stack classifiers for different sets of features. The optimum level of hyperparameters was also given with the help of the same algorithm. The selected pipelines consistently reported a score of more than 99% in all the evaluation metrics used.
Scopus ID: 85084747218
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-5
Conference Paper | Published: November 1, 2019
Abstract
The electroencephalogram (EEG) signals being a recording of the electrical activity of the brain provides valuable information in the analysis of its function and disorder. Epilepsy is a brain disorder characterized by uncontrolled excessive activity. Repeated abnormal disturbance or seizure causes epilepsy. Hence, EEG signals act as a diagnostic tool for epilepsy. An approach based on tree-based pipeline optimization tool (TPOT) is presented for classification of EEG signals as either seizure or normal activity in the brain. A binarized dataset with sampled signal levels and the corresponding class is subjected to a genetic approach for acquiring an optimized predictive model. In TPOT, the tedious process involved in machine learning being repeatedly performed until arriving at the best solution is automated using genetic algorithm, i.e., evaluate-select-crossover-mutate is repeated to tune the pipeline. In the settings used in this paper, there are 90 pipeline configurations for evaluation for which around 450 models are fitted and evaluated against the training data in one grid search. The best pipeline is the one with the highest cross-validation score in the run at 95.94%. The test accuracy is at 95.27% which is just a little lower than the cross-validation score. The predictive model consists of pre-processing steps Maximum Absolute Scaler and Function Transformer which is utilized by a Gaussian Naïve Bayes classifier. The system is trained and tested for epileptic seizure detection using raw EEG signals. The optimized features and predictor obtained via TPOT resulted to a high-performance accuracy for epileptic seizure detection.
Scopus ID: 85084730289
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-6
Conference Paper | Published: November 1, 2019
Abstract
An ideal library makes every last patron feels what it is like to have their own library where one can traverse a world of knowledge, and then build their personal learning environment. Parallel to this conception is the intensification of fine-tuning the conventional librarianship to transform it into a center for new digital learning. As a contribution to this digital revolution, this paper presents an innovative way of renovating the house of dusty books into the center of creativity, research, and partnership through the fusion of traditional librarianship, self-service solutions, and human-computer interaction. The self-service system employs technologies and modalities such as touch screen-assistive technology for the kiosk terminal with the inclusion of a built-in camera, speaker, microphone, and lights, Automatic Speech Recognition, Radio-Frequency Identification and Content-Based Image Retrieval for holdings circulation and monitoring. In conformance with the ISO 9241-210 (Human-centred design for interactive systems), a series of user-centered evaluations were accomplished to obtain early feedback, and to validate that the user requirements have been satisfied at a later stage of the project cycle. To integrate a humanistic approach, the HCSDLC, or Human-Centered Systems Development Life Cycle Methodology, was utilized to complement the four main user-centered design activities specified in the ISO 9241-210 standard. The prototype designs and final self-service library system were assessed in terms of efficiency, effectiveness, and user satisfaction using metrics defined in the ISO/IEC 25022.
Scopus ID: 85084729434
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-6
Juan Miguel H. Villarroel, Jomar B. Calauod, ... Ronald M. Pascual
Conference Paper | Published: November 1, 2019
Abstract
Ideally, instruction is best done one on one. However, due to the scarcity of public school teachers, this ideal remains just that, only an ideal. This ideal, however, can be realized by using a computer-assisted language learning system. One such language that this system can be applied to is the Pangasinense - one of the top ten languages of the Philippines. Using this system, any Filipino can now learn Pangasinense. Creating this involves developing the speech corpus for the Pangasinense language and designing a reading miscue detector (RMD) that employs hidden markov models (HMM) and artificial neural network (ANN). The RMD uses the reference verification (RV) method that compares the input speech to the reference speech found in the Pangasinense speech corpus. The collection of the speech corpus involved 10 native Pangasinense speakers who each recorded a total of 21 phrases and 309 words that were considered as common conversational phrases or words for Pangasinense. The system was initially tested by 10 native Pangasinense speakers, who also speak Filipino, and their scores were set as the reference scores. The system was then put to test by conducting a six-week pilot study participated by 10 Filipino speakers. The system's effectiveness was then evaluated through the progress trends of all learners' scores for each module. All learners' progress curves showed to have a positive slope. In addition, the system's efficiency was determined by its false alarm rate (FAR), misdetection rate (MdR), and accuracy. The system was able to get a FAR of 26.67% and 30%, MdR of 30.0% and 6.67%, and accuracy of 71.66% and 81.67%, for males and females group, respectively.