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Matheus Aparecido do Carmo Alves photo

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followers: 20.0 following: 17.0 repos: 42.0 gists: 0.0

Name: Matheus Aparecido do Carmo Alves

Type: User

Company: Lancaster University

Bio: Computer Scientist currently researching AI-approaches for Scenarios with Uncertainties via Information-based models.

Twitter: mthmoog

Location: São Carlos - SP, Brazil

Matheus Aparecido do Carmo Alves's Projects

2d_fourier_transform icon 2d_fourier_transform

This repository refers to the USP Image Processing module (code: SCC0251) Short Assignment 1: Filtering in Fourier Domain.

behavioral_platform icon behavioral_platform

This repository refers to a collaborative project with professor Emerson Ferreira da Costa Leite (PUC-SP). I have developed a local platform that allows the collection of characteristics and behaviour patterns of users, which is employed to conduct studies and research in the field of Psychology.

cm_plot icon cm_plot

This repository presents the first version of an analytical plot tool for batteries. Developed jointly with Caio Ferreira Bernardo and Alana Aragon Zulke, this is a public version of the project made so far. Any feedbacks and collaborations are welcome as well.

dash_cm_plot icon dash_cm_plot

This repository presents the first version of an interactive plot tool for batteries analysis made in Dash by the Plotly application. Developed jointly with Caio Ferreira Bernardo and Alana Aragon Zulke, this is a public version of the project made so far. Any feedbacks and collaborations are welcome as well.

league_of_predictions icon league_of_predictions

This repository refers to the first project of USP Machine Learning module (code: SCC0276). This project proposes the classification of League of Legends matches winning or losing using the first 10 minutes of the game. We use an MLP implementation trained with improved data to achieve a score of 73% over the test dataset.

lol_api icon lol_api

This repository refers to the final project of USP Introduction to Data Science module (code: SCC0275). We proposed the Personal Context-Aware Recommendation (PCAR) method, which offers to a League of Legend player a personalised recommendation of Picks & Bans based on its statistics on an online and smart-reasoning fashion.

markov_study icon markov_study

This repository refers to my study of Markov Decision Problems. All codes are public and can be used for free for the right purpose. Collaborations and tips are welcome too.

multiagents icon multiagents

This repository refers to a collaborative project between Lancaster University (LU) and University of São Paulo (USP) into Multi-Agents Systems research area. This project is related to Elnaz S. Yourdshahi's work where we research for novel proposals to design reasoning, learning and decision-making processes into an ad-hoc teamwork context.

music_analysis icon music_analysis

This project refers to a paper accepted for publication at the III Congress of the Brazilian Association of Music Theory and Analysis (TeMA) and the IV International Congress of Music and Mathematics (MusMat). Thanks to Danilo Aparecido do Carmo Alves for this opportunity and collaboration.

pest_control icon pest_control

This research project refers to my work in the area of study of embedded Multi-Agents Systems for Pest Control. The project was led by myself and Jo Ueyama, my supervisor and professor at the University of São Paulo (USP), Brazil.

scc0201_icc_ii icon scc0201_icc_ii

Makefile AvailableThis repository refers to the USP Introduction to Computer Science II module (code: SSC0201). In this module, 6 projects were carried out, aiming to implement basic TADs.

scc0207_datascience icon scc0207_datascience

This repository refers to the USP Introduction to Data Science module (code: SCC0275). This module aims to complement the formation of undergraduate students in Computing, maintaining the focus on central concepts and techniques of the Data Science area. The module also intends to introduce theories and methods that enhance the student's knowledge to present solutions to real problems using the studied techniques and tools.

scc0215_arquivos icon scc0215_arquivos

This repository refers to the USP File Organisation module (code: SSC0215). This module aims to provide notions of storage files, indexing techniques, data structure and efficient mechanisms for data recovery in secondary memory.

scc0216_grafos icon scc0216_grafos

This repository refers to the USP Computational Modelling in Graphs module (code: SSC0216). This module aims to teach the importance of graphs in computing, showing their fundamental concepts and main data structures, as well as major applications resolved through graphs.

scc0218_aaa icon scc0218_aaa

This repository refers to the USP Advanced Algorithms and Applications module (code: SCC0218). This module aims to allow the students to be in touch with classic and novel problems in computing that require the analysis of different solutions and programming paradigms.

scc0219_web icon scc0219_web

This repository refers to the USP Introduction to Web Development module (code: SCC0219). This module aims to introduce key concepts in the design of Web applications involving programming and database use.

scc0220_lab_icc_ii icon scc0220_lab_icc_ii

This repository refers to the USP Laboratory of Introduction to Computer Science II module (code: SCC0220).

scc0221_icc_i icon scc0221_icc_i

This repository refers to the USP Introduction to Computer Science I module (code: SSC0221). In this module, 71 exercises were proposed and solved to present basic programming concepts and mathematical logic.

scc0250_cg icon scc0250_cg

This repository refers to the USP Computer Graphics module (code: SCC0250). This module aims to provide an introductory knowledge about computer graphics as well as the practice of basics concepts and applications.

scc0270_redes_neurais icon scc0270_redes_neurais

This repository refers to the USP Neural Networks and Deep Learning module (code: SCC0270). This module aims to present basic concepts of Artificial Neural Networks and the current most important models. To analyze the behavior of these models, their fundamental capabilities and limitations, allowing the use of these techniques to solve practical problems.

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