https://sites.google.com/usp.br/ia-biologia-usp
segunda-feira, 11 de dezembro de 2023
Linhas de Pesquisa com a China - USA e Europa - Draft
1 - Data
Science and Artificial Intelligence (AI) - Data Analytics, Crunching, Minig and
Analysis. ActioBig - Wide Data -. AI: Deductive - Inductive (Supervised,
Non-Supervised, Semi-Supervised and Reinforcement) - Narrow - General -
Diagnostic, Predictive – Prescriptive, Generative, Creative and
Superintelligence - Cross Validation - Split Generalization Index - Accuracy,
Type I Error and II, Specificity, Sensitivity, Recall etc. Statistics: Outliers
Impacts - Machine Learning Check - Small Data - Computational Simulation
Bootstrapping - Jackknife and Monte Carlo. Observational and Experimental.
Endorsing or ratifying AI. Weka, SAS and R programs.
2 - Robotics
with AI and Metaverse - Bots Cbots and Cobots - Fourth and Fifth Industrial
Revolution - Deep Learning – UiPath, Blue Prism and Automation Anywhere.
Metaverse – Virtual and Augmented Reality – Holograms -Avatars – Dimension
Reduction - Disruption: Kakushin and Kaikaku - Perpetual and Persistent
Multi-User Environment - Digital Twins. 3D modeling software, animation
software and game design software. virtual reality (VR) frameworks, augmented
reality (AR) frameworks, and blockchain frameworks. VR headsets, AR glasses and
motion tracking devices. Programming in Python language.
3 - Global
Management Systems and International Quality Certification
Management
and Research 4.0 and 5.0 - International Quality Certification (CIQ) - ISO
standards (9,001 – 14,001 – 22,000 – 17,025 – 17,034 etc.), FSC and GlobalGAP.
Intelligent Management (Smart Farms - Netherlands and Belgium) Ecological and
Multifunctional Farm Management. China Agricultural University and Wageninguen.
Toyota 4.0, Porsche, Lean Startup (Amazon – Uber), Lean Canvas, Lean Six Sigma,
BSC (Apple- Volkswagen – Ford – Citibank - Petrobras – Sabesp) and OKR
(Microsoft – Intel), RCA (NASA). Leadership X and Y by Douglas Mc Gregor.
Pyramid of Human Needs at Work - Maslow, Self-Actualization and the Toyota
Management System. Innovative, Transformational, Digital, Transactional,
Situational and Ethical Leadership. Quality of Life at Work - Self-fulfillment
- Well-Being - Happiness - Shine Project Harvard and World Bank (we coordinate
through USP) - USP Quality of Life Center. Intelligent Management (Smart Farms
- Netherlands and Belgium) Ecological and Multifunctional Farm Management.
China Agricultural University and Wageninguen.
4 - Innovation
Management: Disruptive Innovation, Hyperinnovation Hypercompetitiveness – Blue
Ocean Strategy – Lean Startup. Effectiveness - Efficiency - Competitiveness -
Quality - Productivity – Precision - Accuracy - Survival and Organizational
Growth - Business - Net Profit. Conjoint Analysis – Fractional Factorial
Statistical Models. Quality Function Deployment (Mitsubishi). Risk Management:
FMEA/FTA (NASA) - ISO 31,000 - Information Security - ISO 27,000 – Lean Six
Sigma. Tokenization - Blockchain - Risk Matrix. Text mining on the Internet to
detect weak innovation signals (Iramutec-R and Max QDA), Text Mining -
Lemmatization - Content - Semantics - Linguistics – Discourse. SWOT analysis,
benchmarking and market research. Design thinking, prototyping and testing.
project management, change management and marketing.
Prova 11/12/2023
Prova 11/12/2023
Por favor enviar para o e-mail de exercícios.
Você terá somente que substituir os sinais de interrogação pelos últimos dígitos de seu RG.
DBO | ICobV | ICArb | Bcont | Dis_Pl | IBD_A |
1,604 | 89 | 60 | 11 | 9 | 90 |
0,385 | 90 | 61 | 10 | 8,9 | 91 |
0,216 | 91 | 62 | 9 | 9,1 | 92, |
0,303 | 90 | 59 | 10 | 8,8 | 89 |
1,961 | 20 | 12 | 81 | 0,2 | 20 |
0,782 | 21 | 14 | 79 | 0,3 | 22 |
0,57 | 22 | 15 | 78 | 0,25 | 23 |
2,187 | 22 | 12 | 77 | 0,2 | 24 |
0,764 | 59 | 35 | 41 | 6 | 60 |
0,273 | 60 | 32 | 40 | 6,5 | 61 |
1,883 | 64 | 33 | 38 | 5,8 | 63 |
0,581 | 62 | 32 | 37 | 5,6 | 62 |
0,18 | 79 | 50 | 21 | 8,2 | 80 |
0,007 | 80 | 49 | 20 | 7,8 | 79 |
2,028 | 80 | 48 | 18 | 8,2 | 81 |
2,431 | 79 | 47 | 21 | 7,7 | 78 |
1,604 | 89 | 60 | 11 | 9 | 90 |
segunda-feira, 4 de dezembro de 2023
Exercícios
Exercícios
Enviar os exercícios para o e-mail da disciplina.
E-Mail da Disciplina:
biologia.inteligente.10@gmail.com
Colocar no Assunto do e-mail o Nome Completo e Número do Exercício
Exercícios:
Exercício 1 – Crie um exemplo de regressão linear simples, análogo ao de Concentrações de As a Diferentes Distancias da Rodovia Anchieta. Rode no Excel ou Libreoffice Calc e no SAS com Regressão Robusta. Se não conseguir criar o exemplo acompanhe as aulas de resolução de exercícios, quando Gabriel resolverá os exercícios como se fosse um aluno, você terá somente que substituir os sinais de interrogação pelos últimos dígitos de seu RG. Prazo: 9/10/2023
Dados para Importar:
Distan. | As |
(m) | (mg/kg) |
100 | 0,98??? |
200 | 0,95 |
300 | 0,85 |
400 | 0,86 |
500 | 0,59 |
600 | 0,45 |
700 | 0,32 |
800 | 0,15 |
900 | 0,11 |
1000 | 0,09 |
Exercício 2 - Crie um exemplo de regressão múltipla, análogo ao de Biodiversidade Animal. Se não conseguir criar o exemplo acompanhe as aulas de resolução de exercícios, quando Gabriel resolverá os exercícios como se fosse um aluno, você terá somente que substituir os sinais de interrogação pelos últimos dígitos de seu RG. Prazo 16/10/2023
DBO | ICobV | ICArb | Bcont | Dis_Pl | IBD_A |
1,604 | 89 | 60 | 11 | 9 | 90,??? |
0,385 | 90 | 61 | 10 | 8,9 | 91 |
0,216 | 91 | 62 | 9 | 9,1 | 92 |
0,303 | 90 | 59 | 10 | 8,8 | 89 |
1,961 | 20 | 12 | 81 | 0,2 | 20 |
0,782 | 21 | 14 | 79 | 0,3 | 22 |
0,57 | 22 | 15 | 78 | 0,25 | 23 |
2,187 | 22 | 12 | 77 | 0,2 | 24 |
0,764 | 59 | 35 | 41 | 6 | 60 |
0,273 | 60 | 32 | 40 | 6,5 | 61 |
1,883 | 64 | 33 | 38 | 5,8 | 63 |
0,581 | 62 | 32 | 37 | 5,6 | 62 |
0,18 | 79 | 50 | 21 | 8,2 | 80 |
0,007 | 80 | 49 | 20 | 7,8 | 79 |
2,028 | 80 | 48 | 18 | 8,2 | 81 |
2,431 | 79 | 47 | 21 | 7,7 | 78 |
1,604 | 89 | 60 | 11 | 9 | 90 |
Arquivo para o SAS
data bda;
input DBO ICobV ICArb Bcont Dis_Pl IBD_A;
cards;
1.604 89 60 11 9 90.???
0.385 90 61 10 8.9 91
0.216 91 62 9 9.1 92
0.303 90 59 10 8.8 89
1.961 20 12 81 0.2 20
0.782 21 14 79 0.3 22
0.57 22 15 78 0.25 23
2.187 22 12 77 0.2 24
0.764 59 35 41 6 60
0.273 60 32 40 6.5 61
1.883 64 33 38 5.8 63
0.581 62 32 37 5.6 62
0.18 79 50 21 8.2 80
0.007 80 49 20 7.8 79
2.028 80 48 18 8.2 81
2.431 79 47 21 7.7 78
1.604 89 60 11 9 11
;
/*
input DBO ICobV ICArb Bcont Dis_Pl IBD_A;
*/
proc robustreg;
model IBD_A = DBO ICobV ICArb
Bcont Dis_Pl / diagnostics;
run;
Exercício 3 - Crie um exemplo para rodar cluster analysis. Se não conseguir criar o exemplo acompanhe as aulas de resolução de exercícios, quando Gabriel resolverá os exercícios como se fosse um aluno, você terá somente que substituir os sinais de interrogação pelos últimos dígitos de seu RG Prazo:
data People;
input Categ $ BMI Movm kcal;
cards;
AT 20.5 54.4 3100.???
PR 25.3 2.7 2650
SE 25.6 2.9 2700
SEM 23.1 16.6 2950
;
proc cluster outtree = Dendrog method = average;
var BMI Movm kcal;
id Categ;
run;
PROC TREE DATA = Dendrog;
RUN;
Exercício 4 - Crie um exemplo para rodar uma Rede Neural no Weka. Se não conseguir criar o exemplo acompanhe as aulas de resolução de exercícios, quando Gabriel resolverá os exercícios como se fosse um aluno, você terá somente que substituir os sinais de interrogação pelos últimos dígitos de seu RG. Prazo 13/11???
@RELATION Hantav
@ATTRIBUTE MasaCorp REAL
@ATTRIBUTE CompCaud REAL
@ATTRIBUTE OuviExt REAL
@ATTRIBUTE PataPost REAL
@ATTRIBUTE PataAnte REAL
@ATTRIBUTE CorpCabe REAL
@ATTRIBUTE Class {0,1}
@DATA
107.4,91.7,13.11,27,19.4,141.???,0
107.3,90.79,12.97,24.3,19,141.7,0
107.4,90.79,12.97,26.1,19.2,142.2,0
107.4,90.79,12.96,22.5,20.8,142,0
107.5,90.72,12.97,31.2,15.4,141.8,0
107.85,91.56,13.05,27,20.2,141.4,0
107.75,90.65,12.97,23.7,19.2,141.6,0
107.25,90.72,12.92,21.6,21.4,141.7,0
107.45,90.58,12.97,24.6,22,141.9,0
107.6,91.28,13.03,27.6,20,140.7,0
107.65,91.28,13.03,23.7,23.4,141.8,0
107.55,90.65,12.96,23.1,21,142.2,0
107.6,91.56,12.96,23.7,21.6,141.4,0
107.35,90.79,12.97,23.1,21.8,141.7,0
107.55,90.93,12.97,23.1,21.6,141.8,0
107.25,90.86,12.98,27.9,17,141.6,0
107.3,90.93,13.01,24.6,19.6,141.7,0
107.5,90.93,12.97,27,18,141.9,0
107.6,90.72,12.96,22.2,23,141.5,0
107.35,91.14,12.99,25.8,20,141.9,0
107.5,90.93,12.93,25.2,20,141.4,0
107.8,91.35,13,24.3,20.6,141.6,0
107.65,91.42,13,25.2,21.6,141.5,0
107.85,91.14,13,26.1,20,141.6,0
107.55,90.79,12.99,22.2,21.6,141.1,0
107.65,91.28,13.04,24,22,142.3,0
107.75,91.14,13.01,26.7,19.6,142.4,0
107.55,91.21,13.03,29.4,19,141.9,0
107.55,91,13,22.2,21,141.8,0
107.4,90.79,12.93,24.9,18,142,0
107.6,91.07,12.98,23.7,21.4,141.8,0
107.4,90.79,12.97,25.8,18.2,142.3,0
107.5,91,12.96,23.1,21,140.7,0
107.8,91.28,13.01,25.2,20.6,141,0
107.95,91.28,13,26.7,21.2,141.4,0
107.3,91.14,13.02,28.2,19.4,141.8,0
107.75,91.21,13,25.2,19.4,141.8,0
107.65,90.93,12.94,23.7,20,142,0
107.65,91.21,13.01,25.5,18.6,142.1,0
106.95,91.21,12.9,24.3,19.4,141.3,0
107.2,90.86,12.92,26.7,18.8,142.3,0
107.4,91.07,12.96,26.4,19.8,140.9,0
107.45,90.72,12.94,27.9,18,141.7,0
107.45,91.28,12.97,27,19.6,140.9,0
107.4,90.58,12.91,24.6,20.4,141,0
107.15,90.65,12.94,24.9,20.4,141.8,0
107.4,90.93,12.97,24.9,20.4,141.5,0
107.4,90.93,12.97,21.9,21.8,142,0
107.3,90.79,12.98,23.7,20.6,141.1,0
107.25,90.3,12.96,23.4,19.6,142,0
107.3,90.86,12.94,21.6,20,141.3,0
107.65,91.42,13,28.5,19.4,141.1,0
107.25,91.07,13,23.4,21.8,140.9,0
107.7,91.14,13.02,22.8,21.8,141.6,0
107.25,90.58,12.95,23.7,20,141.4,0
107.6,90.79,12.94,27.6,18.8,142,0
107.85,91,12.94,27.6,20.8,141.2,0
107.5,90.72,12.94,26.4,18,141.1,0
107.55,91.07,12.99,23.7,22,141.3,0
107.55,91,12.98,24.6,20.6,141.4,0
107.55,90.72,12.93,24.9,19.8,141.6,0
107.65,90.79,12.94,22.5,21,141.5,0
107.7,90.86,12.94,24,21.2,141.5,0
107.25,91,12.95,24,21.6,141.4,0
107.5,91,12.98,25.8,21.2,141.5,0
107.6,91.42,13,26.4,21.2,140.8,0
107.3,90.65,12.92,23.1,20.6,141.3,0
107.4,90.79,12.93,27.3,19,141.5,0
107.55,90.72,12.98,25.8,19.6,141.8,0
107.45,91.14,13.02,24,22.4,139.6,0
106.9,90.86,12.95,25.2,22.2,140.9,0
107.6,90.93,12.95,24.6,20.6,141.4,0
107.5,90.72,13.02,26.1,20,141.2,0
107.2,90.93,12.96,22.5,21,141.8,0
107.6,90.93,12.97,21.6,21.2,142.1,0
107.05,90.72,12.93,22.8,21.4,141.7,0
107.45,90.93,13.01,26.4,20,141.2,0
107.3,90.86,12.94,22.2,21.2,141,0
107.6,91.35,12.98,23.7,21.8,140.9,0
107.3,90.93,12.94,23.7,20,141.8,0
107.55,90.79,12.97,25.8,20.6,140.6,0
107.45,90.86,12.96,22.5,20.6,141,0
107.6,90.79,12.91,27,19.4,141.9,0
107.6,91.07,12.99,23.7,21.6,141.3,0
107.7,91.49,13.02,27,22.2,141.2,0
107.55,90.93,12.96,26.7,20.4,141.5,0
107.6,90.93,12.97,26.1,19,141.6,0
107.5,90.72,12.92,25.2,20.4,142.1,0
107.45,91.21,12.99,22.2,22.4,141.5,0
107.5,90.93,12.97,24,21,142,0
107.35,90.79,12.93,25.8,19.2,141.6,0
107.7,91,12.99,25.5,19.4,141.4,0
107.45,90.58,12.95,24.6,19.8,141.5,0
107.25,90.65,12.93,22.2,21.4,141.5,0
107.35,90.72,12.95,24.9,20,142,0
107.8,90.93,12.99,27,19,141.7,0
107.5,91.28,13.03,27.3,20.4,141.1,0
107.2,90.79,12.95,24,20.6,141.2,0
107.55,91,12.98,27.3,20.4,141.5,0
107.35,91,12.94,23.4,20,141.2,0
107.2,91.07,13.03,29.1,23.4,139.8,1
107.45,91.35,13.02,33,23,139.5,1
107.45,91.21,13.01,26.1,23.4,140.2,1
107.5,91.28,13.06,29.7,21.8,140.3,1
107.35,91.14,13.03,35.4,21.8,139.7,1
107.5,91.14,13.02,31.8,21.4,139.9,1
107.65,91.21,13.01,27.9,24.2,140.2,1
107.4,91.07,13.04,29.4,23,139.9,1
107.5,91.14,12.99,30,23.8,139.4,1
107.6,91.42,13.08,31.2,22.4,140.3,1
107.6,91.28,13.03,24,23,139.2,1
107.55,91.35,13.03,31.8,23,140.1,1
107.7,91.49,13.11,29.1,23.6,140.6,1
107.45,91.28,12.99,34.2,22,139.9,1
107.55,91.21,13,31.8,21.6,139.7,1
107.75,91.28,13,24.6,22.4,139.2,1
107.35,91.42,13.01,35.4,21,139.8,1
107.35,91.28,13.01,36.3,20.8,139.9,1
107.4,91.35,13.02,33,22,140,1
107.2,91.14,12.99,30.3,24,139.2,1
107.4,91.21,13.04,30.3,24.2,139.6,1
107.55,91.42,13.03,36.9,20.4,139.6,1
107.65,91.56,13.11,34.8,21.2,140.2,1
107.55,91.49,13.04,31.5,22.4,139.7,1
107.35,91.35,13.05,29.7,20.6,140.1,1
107.45,91,13.03,30.6,22.8,139.6,1
107.5,91.28,13.04,28.2,23.2,140.2,1
107.75,91.49,13.03,30.6,23.6,140,1
107.55,91.14,13.02,30.3,22.6,140.3,1
107.25,91.14,13.06,29.4,24.2,139.9,1
107.15,91.14,13,32.1,21,139.8,1
107.25,91.14,12.98,36.9,22.4,139.2,1
107.45,91.35,13.02,31.8,23,139.9,1
107.3,91.14,13.04,31.5,23.6,139.7,1
107.1,91,13.02,33,22.4,139.5,1
107.4,91.07,13.01,35.7,22.2,139.5,1
107.3,90.86,13.02,32.1,22.2,139.4,1
107.45,91.49,13.03,27.9,22.4,138.3,1
107.3,91.28,13.04,33.9,21.6,139.8,1
107.25,91.35,13.02,35.4,20.4,139.6,1
107.4,91.14,13.03,30,23.8,139.3,1
107.35,91,12.94,30.6,22,139.2,1
107.3,91.14,13.04,33.6,21.4,139.9,1
107.5,91.35,13.04,31.8,22.2,139.9,1
107.25,90.86,12.98,34.2,20,139.3,1
107.45,91.42,13.04,35.7,21,139.8,1
107.5,91.35,13.04,34.2,21.4,139.9,1
107.65,91.42,13.03,27.9,22.6,138.1,1
107.35,91.14,13.01,32.1,22,139.4,1
107.45,90.93,13,29.7,24.6,139.4,1
107.45,91.21,12.99,35.7,21.2,139.8,1
107.3,90.93,12.97,35.7,20.2,139,1
107.3,90.79,12.93,31.2,22,139.3,1
107.25,91.07,13.01,36.3,20.6,139.4,1
107.25,91.21,13,33,23,139.5,1
107.55,91,13.03,34.8,21,139.7,1
107.1,90.79,12.96,30.9,22.8,139.5,1
107.2,91.07,13,33.9,21.4,139.2,1
107.4,91.28,13.06,37.5,20,139.3,1
107.3,91.42,13.01,24.3,24.2,137.9,1
107.8,91.07,12.97,22.2,24.4,138.4,1
107.45,91.35,13.01,29.7,20.4,138.1,1
107.3,91.07,13,34.5,21.2,139.5,1
107.35,91.07,13.02,34.8,21.8,139.1,1
107.15,91.21,13,34.2,21,139.8,1
107.55,91.21,13.06,30.9,24,139.7,1
108.15,91.49,13.04,30,20.2,138.8,1
107.8,91.28,13.01,28.8,22.4,138.6,1
107.4,90.93,12.98,28.8,24,139.6,1
107.45,91,12.99,34.2,21.8,139.7,1
106.95,91.49,13.05,26.1,23,137.8,1
107.1,91.42,13.04,36,20.4,139.6,1
107.4,91.35,13.03,35.4,21,139.4,1
107.4,90.72,13,31.2,23.2,139.2,1
107.4,91.07,13,34.2,21,139.6,1
107.45,91.28,13.02,35.7,21.4,139,1
107.15,91.07,13.01,34.8,21,139.7,1
107.25,91.28,13,29.7,24,139.6,1
107.4,91.35,13.03,30.6,24.2,139.1,1
107.25,91.14,13.04,24.6,23.6,137.8,1
107.5,91.28,13.01,34.2,21.4,139.1,1
107.4,91.42,13.06,24,22.8,138.7,1
107.5,91.35,13.01,33,22.8,139.3,1
107.3,91.35,13.04,30.3,22.8,139.3,1
107.35,91.14,13.01,32.1,22.2,139.5,1
107.35,91.28,13,34.5,21.4,139.4,1
107.25,91.28,13,24,24.4,138.5,1
107.4,91,12.97,34.2,21.2,139.2,1
107.4,90.93,13.02,28.8,23.8,139.4,1
107.3,91.21,13.02,38.1,18.2,139.2,1
107.55,91.14,12.98,30.6,24,139.4,1
107.7,91.35,13.06,26.4,22,138.6,1
107.35,91.21,13.02,32.4,22.2,139.2,1
107.5,91.35,13.03,28.8,22,138.5,1
107.45,91.21,13.05,34.8,21.2,139.8,1
107.5,91.28,13.03,29.7,24.2,139.6,1
107.55,91.21,12.99,30.9,23,139.7,1
107.4,91.21,13.04,31.8,22.2,140,1
107.35,91.49,13.08,33.6,22.4,139.4,1
107.15,90.93,12.99,30.6,23,139.6,1
Exercício 5 - Elaborar um seminário,
- Utilidade Inteligência Artificial (IA) para o Biólogo
- Utilidade Ciência de Dados (CD) para o Biólogo
- Utilidade Simulação Computacional para o Biólogo (Bootstrapping, Jackknife, Monte Carlo etc.)
- Utilidade Mineração de Texto – IA - CD (Analises Linguísticas) para o Biólogo
- Utilidade Metaverso para o Biólogo
- Elaborar de 5 a 7 slides, individual.
- Parâmetros: 5 a 7 linhas, fonte 28, mínimo, uma palavra da origem a 10 ou 20 na oratória. Colocar símbolos visuais (lado direito do cérebro), também cores, gráficos e diagramas, não tabelas, números (lado esquerdo)
- Gavar um vídeo ou texto em Word, Libre Office Writer similar a apresentação para China.
- PRAZO 6/11/2023
Exercício 6 - Optativo - Aplicar ANOVA E MANOVA no Exercício 3
data People;
/* BMI: body mass index --> Índice de M. Corporal = Peso / (Altura * Altura)
Movm: Movement (Km por semana)
KCal : Kilocalories (ingeridas por dia)
ATL: Athletes
SEMI: Semi-athletes
SEDE: Sedentary
PROF: Professor
*/
input Categ $ BMI Movm Kcal;
cards;
ATL 20.2 60.7 3200
ATL 21.3 54.8 3100
ATL 19.3 49.6 2800
ATL 21.1 52.3 3300
SEMI 22.4 14.9 2600
SEMI 21.9 17.8 2700
SEMI 23.8 18.6 3200
SEMI 24.1 15.1 3300
SEDE 27.3 2.5 2700
SEDE 23.4 4.3 2300
SEDE 25.2 2.3 2600
SEDE 26.4 2.6 3200
PROF 26.2 4.1 2600
PROF 24.2 2.1 2700
PROF 25.4 1.9 2650
;
Proc ANOVA;
Class Categ;
Model BMI Movm Kcal = Categ;
Means Categ / Duncan Lines;
Run;
Exercicio 7 - Optativo. Anal. os dados do exercicio 2 com IA no progrma Weka
Assinar:
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