FEU Institute of Technology

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Brylle Miguel E. Andres

Student

Hi

Antipolo, Rizal Β· FEU Institute of Technology

Personal Information

Short Biography

A motivated Computer Science student specializing in Software Engineering, seeking to leverage theoretical knowledge and programming skills to contribute effectively to innovative software development projects. Eager to apply classroom learning to real-world challenges while gaining practical experience and furthering technical expertise in a professional setting.

πŸ› οΈ Skills

Microsoft Word

Expert (85%)

Microsoft Excel

Expert (90%)

Quality Assurance

Expert (85%)

Data Analytics

Competent (70%)

PHP

Beginner (60%)

πŸŽ“ Educational Qualification

Tertiary Β· Aug 2018 - Present

Bachelor of Science in Computer Science

Software Engineering Β· Far Eastern University - FEU Institute of Technology

Secondary Β· Jun 2017 - Mar 2018

Sacred Heart Catholic School of Cainta

Secondary Β· Jun 2016 - Mar 2017

STI College Ortigas-Cainta

πŸ† Honors and Awards

Best Thesis (Education) (Academic)

Issued by FEU Tech Computer Science Department on November 30, 2023

1st Runner-Up

Welcome Celebration: iTamsFIT 2018 (Sports)

Issued by FEU Tech Student Coordinating Council on August 29, 2018

πŸ“œ Licenses and Certifications

DevNet Associate

Issued by Cisco on August 03, 2022

View Credential

IT Specialist - Python

Issued by Certiport on March 22, 2022

View Credential

MTA: Introduction to Programming Using Java

Issued by Microsoft on March 28, 2021

View Credential

πŸ‘¨πŸ»β€πŸ« Seminars and Trainings

Internship Training 1

Awarded by GHD on August 20, 2024

View Credential

SHS Work Immersion

Awarded by Sacred Heart Catholic School of Cainta on March 09, 2018

πŸ‘₯ Organizations and Memberships

FEU Tech Artist Connection

Senior Member Β· September 15, 2018 - Present

FEU Tech Association for Computing Machinery

Member Β· August 30, 2018 - July 08, 2019

Research Publications

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Conference Paper Β· 10.1109/hnicem64917.2024.11258707

Augmentative and Alternative Communication Tutor for Filipino Preschoolers: A Tool for Predicting Rapid Guessing Using Decision Tree

2024 IEEE 16th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2025), pp. 1-6

Angelo C. Arguson Angelo C. Arguson , Jose Ian Miguel S. De Leon, ... Mark Revin G. Fragata
View Paper

This study centers on the essential role played by Speech-Language Pathologists (SLPs) in the diagnosis and treatment of speech and language disorders within the Philippines. It underscores the significant difficulties resulting from the limited availability and effectiveness of Augmentative and Alternative Communication (AAC) tools, particularly in the context of the Filipino language. These limitations impede the progress of Filipino children struggling with speech delay disorders. The study aims to develop AAC software integrated with an intelligent tutoring system in Filipino. This innovative approach incorporates Filipino AAC tools such as AAC boards, assessments, client management, and identification of rapidguessing behavior on AAC assessments on different difficulty levels using a decision tree algorithm, providing a structured and personalized therapy approach. The software was evaluated using FURPS with a total of 50 participants, whom are the 30 or 60 % speech-language pathologists, 8 or 16 % Information Technology and Computer Science (IT/CS) professionals, 2 or 4 % CS Professors, and 10 or 20 % Parents/Guardians. The computed Cronbach's alpha (Ξ± ) was 0.95 which indicates the FURPS instrument has excellent internal consistency. The grand mean of the software evaluation was rated at 4.63 which highlights the generally positive evaluation of the system. Precision, recall and F1-score assess the model's performance in binary classification. For the class labeled β€œ0,” the model achieved a precision of 0.99, a recall of 1, and an F1 score of 0.99. This indicates that the model has high accuracy in predicting instances belonging to class β€œ0.” For the class labeled β€œ1,” the model achieved a precision of 0.95, a recall of 0.92, and an F1-score of 0.93, indicating slightly lower performance than class β€œ0.” The findings of this study and the developed software have significant implications in the field of AAC. Additionally, this study's contribution serves as a foundation for future advancements in AAC-related technologies, driving innovation and improvement in the field.

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