Scopus ID: 85062896790
Proceedings of the 6th International Conference on Information Technology: IoT and Smart City, (2018), pp. 31-36
Jennifer O. Contreras, Melvin A. Ballera, ... Jennalyn G. Raviz
Conference Paper | Published: December 29, 2018
Abstract
Nowadays, a lot of Filipinos are keen into traveling locally and abroad that encourages different airline companies to enhance their services to gain more clients. In this study, we extracted Twitter tweets about the three major airlines in the country today, Cebu Pacific, Air Asia, and Philippine Airlines. Various models, techniques and classifiers were introduced in computing the sentiment scores of the Tweets gathered which is useful to different fields like education and businesses. This study aims to apply a new approach in implementing sentiment analysis task thru pattern matching using a regular expression and implementing lexicon-based scoring of sentiments. Its main purpose is to assign the correct polarity to get the sentiments of the Filipino travelers.
Scopus ID: 85062901737
Proceedings of the 6th International Conference on Information Technology: IoT and Smart City, (2018), pp. 267-271
Conference Paper | Published: December 29, 2018
Abstract
In theory of computation, a deterministic finite automaton (DFA) is a finite state machine that accepts/rejects finite strings of symbols and only produces a unique computation. This study aims to convert the extracted decision tree rules sets from decision tree algorithm and the learning path sequence of the learning management system. This paper converts decision tree and learning management system rules into deterministic finite automaton. The decision tree rule sets are rule sets used to predict the vulnerability of not having graduation on time. The learning management system rules are the backbones of the learning management system in order to provide individualized learning scheme tailored to the preferences of the learners. The conversion of decision tree rules of student graduation and adaptive learning environment lead to easier interpretation and visualization of the methods and processes involved.
Scopus ID: 85057242059
IOP Conference Series: Materials Science and Engineering, (2018), Vol. 431, pp. 072009
Nolan C. Concha
& Andres Winston C. OretaJournal Article | Published: November 15, 2018
Abstract
o monitor the initiation of concrete cracking beyond the service life of the structure, a novel prediction model of time to cracking of concrete cover using artificial neural network (ANN) was developed in this study. Crack mitigation prevents corrosion and crack development to occur in a more rapid phase that is an essential component in performance-based durability design of reinforced concrete structures. Data available in various literatures were used in the development of the ANN model which is a function of compressive strength, tensile strength, concrete cover, rebar diameter, and current density. The neural network model was able to provide reasonable results in time predictions of cracking of concrete protective cover due to formations of corrosion products. The performance of ANN model was also compared to various analytical and empirical models and was found to provide better prediction results. Even with limitations in the available training data, the ANN model performed well in simulating cracking of concrete due to reinforcement corrosion.
Scopus ID: 85064127351
2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM), (2018), pp. 1-5
Conference Paper | Published: July 2, 2018
Abstract
The combination of multimedia technology and Internet provides an amiss channel for pornographic contents accessible by certain sensitive groups of people. Furthermore, the same channel provides the easiest medium to distribute illicit images and videos without an autonomous content supervision process. In this study, an application was developed grounded from a pixel-based approach and a skin tone detection filter to identify images and videos with a large skin color count and considered as pornographic in nature. With nudity detection algorithm as the foundation of the system, all multimedia files were preprocessed, segmented, and filtered to analyze skin-colored pixels by processing in YCbCr space and then classifying it as skin or non-skin pixels. Afterwards, the percentage of skin pixels relative to the size of the frames is calculated to be part of the mean baseline for nudity and non-nudity materials. Lastly, the application classifies the files as nude or not, and then filter it. The application was evaluated by supplying a dataset of 1,239 multimedia files (Images = 986; Videos = 253) collected from the Web. On the final testing set, the application obtained a precision of 90.33% and accuracy of 80.23% using the supplied dataset.
Scopus ID: 85064106284
2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM), (2018), pp. 1-4
Conference Paper | Published: July 2, 2018
Scopus ID: 85064126714
2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM), (2018), pp. 1-6
Francis B. Malit, Irister M. Javel, ... Melodia Pahati
Conference Paper | Published: July 2, 2018
Abstract
A design of a secondary power supply using Proportional-Integrative-Derivative (PID) controller has been implemented for better compensation, response and output in comparison with a conventional control strategy of a switch mode converter. A boost converter for an automobile battery as a power source with adaptive control has been introduced in the design. The offsetting and joggling of a 3D printer is manifested as electronic load during the testing. An analysis about the modes of controlling a switch mode power supply (SMPS) has been made using: (1) MATLAB for simulation of graphical presentation of the method and for analysis of transient response of the design and (2) NI Multisim for simulation of SMPS circuit including PID control and conventional SMPS circuit with a fundamental compensation circuit. The two approaches are compared in this paper. The simulations have shown that PID compensates for error in output response and decreases the response time of the converter circuit. Overall the entire design scheme is successfully verified through simulations and by prototyping that shown improvement on dynamic performance (response and efficiency) as compared with the conventional scheme. With the use of adaptive controller, the performance of the boost converter has improved mainly with reduced its response time (3.64ms settling time and 467μs peak time) ensuring its effectiveness in adapting to changes in the power demanded by the load.
Scopus ID: 85064124861
2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM), (2018), pp. 1-5
Conference Paper | Published: July 2, 2018
Abstract
Predicting the compressive strength of self-compacting concrete (SCC) is one of the complicated tasks because of its complex behavior due to the reaction of chemical and mineral content and the hydration process of cement. The distinct difference of SCC to normal concrete is its improved workability or also known as rheology that is divided into four categories namely viscosity, flow ability, passing ability, and resistance to segregation. It was proposed in this study to include the rheological behavior of SCC to the prediction of its compressive strength. Neural network was utilized for predicting the 28th day compressive strength of SCC. A 97.78% prediction rate was achieved using a feed-forward back-propagation ANN with 1 hidden layer and 8 hidden nodes. Tansig transfer function was used as activation function. The model has a Pearson R value of 0.991 and mean square error (MSE) of 3.42.
Scopus ID: 85064126672
2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM), (2018), pp. 1-6
Conference Paper | Published: July 2, 2018
Scopus ID: 85063188183
TENCON 2018 - 2018 IEEE Region 10 Conference, (2018)
Conference Paper | Published: July 2, 2018
Abstract
One of the predominant cause of the diminishing productivity of the Philippines that affects its residents and industry sectors alike is no other than the unresolved traffic. Numerous efforts have been implemented in the country to regulate traffic including road expansion, highway development and application of several traffic schemes. One of the research thrust being studied is the solution to the limitation of traditional traffic light systems. Existing literatures in traffic light system embarked on intelligent transportation system (ITS) that is typically based its operation on real-time traffic density data, however implemented in limited control. This paper discussed an approach in developing traffic signaling system capable of prioritizing congested lanes based on real-time traffic density data and integrated with an automated and manual control ported in a mobile android-based application. The system worked with CCTV cameras positioned at every lane of the intersection for the acquisition of traffic images transmitted to the Raspberry Pi 3 microcontroller for traffic density calculation using image processing. It utilized a traffic monitoring system and traffic lights operation control via a mobile android-based application. The system was tested and yielded an average of 92.83% and 85.77% vehicle detection rate for daytime and nighttime respectively. Moreover, an overall system reliability of 92.82% and 85.77% were obtained during daytime and nighttime testing based on the android GUI, lane prioritization and traffic light response. Future work involved integrating the Internet of Things (IoT) on the traffic light system for a wider scope interconnected implementation.
Scopus ID: 85063226839
TENCON 2018 - 2018 IEEE Region 10 Conference, (2018), pp. 0194-0198
Conference Paper | Published: July 2, 2018
Abstract
This paper presents a classification algorithm for air quality index (AQI) using fuzzy logic (FL) system. AQI tells the level of cleanliness of the air and provides a corresponding health warning. In this study, two types of input pollutants are only considered which are the carbon monoxide (CO) and nitrogen dioxide (NO2). Each input is classified into six categories that include very low, low, moderate, high, very high and extremely high. Mamdani fuzzy inference system (FIS) is used to process the FL system giving an output of AQI values expressed in six categories: good, moderate, unhealthy for sensitive groups, unhealthy, very unhealthy and hazardous. Simulation is performed using MATLAB fuzzy logic toolbox, which provides effective and reliable results.