Ace C. Lagman
95 Publications
Scopus ID: 85084485556
International Journal of Scientific & Technology Research, (2020), Vol. 9, No. 4, pp. 1318-1322
Journal Article | Published: January 1, 2020
Abstract
Blood banking is the process of collecting, separating and warehousing blood. There are numerous file-based repositories of blood bank management that exist for storing data for blood bank ecosystem such as hospitals and centers. This functions for maintaining the information of donors, availability of blood, and transaction information. Currently, these systems are effort intensive, costly, and failed to achieve efficiency in terms of its filtering mechanism which makes repository penetrating faster and reliable. This paper introduces a new design for blood banking ecosystem with proper filtering solution using frame-based approach. The system has three major features: (1) blood camp setup module, (2) stocks management module which includes the blood donation and blood releasing, and (3) the filtering system module which shows the nearest blood camp with the available blood type based on the patients’ needs. Also, with the use of frame-based approach as filtering method, the system is more efficient and reliable compared to other blood banking repository systems. The system’s functionality was tested for its efficiency, usability, and reliability and the results are revealed in the survey. Conclusions and future work were also provided in this paper.
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: 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: 85084749145
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019)
Conference Paper | Published: November 1, 2019
Abstract
The Internet of Things (IoT) and Blockchain are increasingly growing focus on research. Blockchain is like the Internet when we first knew of it. This new technology could revolutionize how we do everything. Seriously, everything. Think how the Internet changed our daily lives today. And just like that, we don't really have to understand the technology behind it and be knowledge of its importance. But what makes this innovation revolution a bigger deal than Bitcoin and possibly even the Internet itself, are the exponential opportunities the concept provides. However, the potential to integrated Blockchain to Internet of Things (IoT) has constraint due to the required high computational power. IoT comprises of numerous platforms which have limited performance. Usually, these platforms cannot handle intensive procedures and often has scalability issues. In this paper, we give an overview of consensus algorithms and the necessity of this method to blockchain technology. Moreover, we focused on three of the utmost common consensus algorithms used in blockchain technology and explore their potential adaptation in an IoT framework with respect to its requirements.
Scopus ID: 85091343437
Korean Journal of Remote Sensing, (2019), Vol. 35, No. 4, pp. 561-571
Journal Article | Published: August 31, 2019
Abstract
In this paper, we analyze the effect of the representative spectral indices, normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and normalized difference built-up index (NDBI) on classification accuracies of Landsat-8 OLI image. After creating these spectral index images, we propose five methods to select the spectral index images as classification features together with Landsat-8 OLI bands from 1 to 7. From the experiments we observed that when the spectral index image of NDVI or NDWI is used as one of the classification features together with the Landsat-8 OLI bands from 1 to 7, we can obtain higher overall accuracy and kappa coefficient than the method using only Landsat-8 OLI 7 bands. In contrast, the classification method, which selected only NDBI as classification feature together with Landsat-8 OLI 7 bands did not show the improvement in classification accuracies.
Scopus ID: 85078365451
2019 International Symposium on Multimedia and Communication Technology (ISMAC), (2019), pp. 1-6
Conference Paper | Published: August 1, 2019
Abstract
This study utilized data mining and geo-mapping methods to develop a fire risk management system for the Bureau of Fire Protection (BFP) in the city of Manila. This system was integrated into a web portal where the BFP personnel can log and view fire incident reports, which are then evaluated and mined for marking fire “hot spots” on a customized map of the city, as well as for producing recommendations based on the risk level assessment of any location for a given date. Based on results of experimentation, the Decision Tree classifier model was selected, with 95.92% accuracy. Geocoding produced 92% output of geographical coordinates from address information in the data set. The system can help the fire agency in raising the fire risk awareness of the community country and in facilitating their fire risk reduction planning.
Scopus ID: 85072989545
2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), (2019), pp. 149-153
Conference Paper | Published: February 1, 2019
Abstract
Organizations are challenged to achieve effective and competent results, rising to imminent importance of measuring the performance efficiency. Data Envelopment Analysis (DEA) is an approach that measures performance efficiency of organizations. It is a non-parametric method, which uses linear programming to calculate efficiency in a given set of decision-making units (DMUs). It has widespread application in identifying efficiency and discovering benchmark. In the study, it utilized DEA in identifying School-Based Management (SBM) performance efficiency of one (1) division comprising of elementary and secondary schools - under Department of Education (DepEd) in the Philippines. Efficient schools were used as benchmark for improvement of inefficient schools. The schools had also undergone clustering, which is the process of grouping in accordance to similar characteristics. K-Means clustering algorithm was used to group the schools according to their respective profile. K-Means clustering is a simple unsupervised learning algorithm that follows a simple procedure of classifying a given data set into a number of clusters. The study also encompasses the development of an application system that utilizes data from DEA and K-Means clustering algorithm. The application system also provided recommendations to help inefficient schools improve.
Scopus ID: 85072963741
2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), (2019), pp. 414-418
Conference Paper | Published: February 1, 2019
Abstract
Advances in computing and the availability of massive health data are opening up new possibilities for the generation of helpful decision-support tools. Forecasting the incidence of medical cases, which is one of the first steps in institutional planning, plays an important role in planning health control strategies in order to develop intervention programs and allocate resources. This study focused on medical cases forecasting for the development of resource allocation recommender system. Data cleaning was performed in the historical data of medical cases from Mabalacat City Health Office in order to detect and removing corrupt and inaccurate records. The forecasting models used are Seasonal Auto-Regressive Integrated Moving Average (S-ARIMA) and Exponential Smoothing (ES). Factor values of twelve (12) for monthly seasonality and four (4) for quarterly seasonality were used for the S-ARIMA models. The alpha values used in ES are 0.1, 0.3, 0.5, 0.7 and 0.9. The computed Mean Absolute Deviation (MAD) and the Mean Absolute Percent Error (MAPE) results of S-ARIMA and ES were compared and the forecasting model with the better accuracy was used for a particular medical case forecast value. The use of the mentioned forecasting algorithms and accuracy tests were embedded in the development of an online information system with resource allocation recommender for Mabalacat City health units.
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: 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.