Rocky5502

A-Comprehensive-Literature-Review-on-Integrating-Personality-Profiling

In recent years, the advent of LLMs, such as OpenAI's GPT-3 [30] and Google's LaMDA [12], has revolutionized natural language processing (NLP) and opened up new possibilities for developing advanced recommendation systems. LLMs possess the remarkable ability to generate creative text, translate languages,

AI-for-Medicine-Specialization

AI for Medicine Specialization - Coursera offered by deeplearning.ai

BUAA-DataStructures-2023-Project

Beihang University, Data Structure-2023 Project for Postgraduate International and Chinese Students.

Cluster-Analysis-in-Python

unsupervised learning through clustering using the SciPy library in Python.

Data-Science-capstone

Capstone: Milestone Report

datacamp_data_scientist_with_python_career_track

This repo includes the course notes for "Data Scientist with Python"

Datacamp_DataScienceTrack_Python

datacamp track: Data Scientist with Python (2019) Code & Data

datasicencecoursera

Data science

DataViz

Data Visualization With Matplotlib and Seaborn

Web-Data-Mining-Project-2021-SCU-

In this work, To implement this project we used Python Programming language and Libraries: Scikit-Learn/SciPy, NumPy, Pandas, Matplotlibwe carried out and evaluated a classification algorithm to notice 4 fundamental human physical activities(walking, cycling, sitting, and lying) using five triaxial accelerometers worn simultaneously on unique parts of thebody (dominant hip, higher arm, ankle, thigh, and wrist). The accelerometer information was collected, cleaned, and preprocessed to extract elements from the 10 s window. These time and frequency area aspects had been used with Random Forest and k-Nearest Neighbour classifier to classify challenge activities. The algorithms had been evaluated based totallyon Leave-One-Subject-Out (LOSO) and ten-fold cross-validation method the usage of each accelerometer records as well as annotated undertaking labels from 33 contributors in a lab. Random Forest showed the first-rate overall performance recognizing the activities with common accuracy of 89 % for the LOSO approach for hip data. Combining statistics from both hip and ankle improved the common accuracy with the aid of 3.5 %, and by using 10% for mendacity activity, which had the lowest classification accuracy (80%) for hip data. We conclude that our algorithm that makes use of 10 aspects suggests right endeavor classification, and is computationally efficient to be carried out in real-time cell systems.

Rocky5502/Web-Data-Mining-Project-2021-SCU-

In this work, To implement this project we used Python Programming language and Libraries: Scikit-Learn/SciPy, NumPy, Pandas, Matplotlibwe carried out and evaluated a classification algorithm to notice 4 fundamental human physical activities(walking, cycling, sitting, and lying) using five triaxial accelerometers worn simultaneously on unique parts of thebody (dominant hip, higher arm, ankle, thigh, and wrist). The accelerometer information was collected, cleaned, and preprocessed to extract elements from the 10 s window. These time and frequency area aspects had been used with Random Forest and k-Nearest Neighbour classifier to classify challenge activities. The algorithms had been evaluated based totallyon Leave-One-Subject-Out (LOSO) and ten-fold cross-validation method the usage of each accelerometer records as well as annotated undertaking labels from 33 contributors in a lab. Random Forest showed the first-rate overall performance recognizing the activities with common accuracy of 89 % for the LOSO approach for hip data. Combining statistics from both hip and ankle improved the common accuracy with the aid of 3.5 %, and by using 10% for mendacity activity, which had the lowest classification accuracy (80%) for hip data. We conclude that our algorithm that makes use of 10 aspects suggests right endeavor classification, and is computationally efficient to be carried out in real-time cell systems.

Rocky5502/A-Comprehensive-Literature-Review-on-Integrating-Personality-Profiling

In recent years, the advent of LLMs, such as OpenAI's GPT-3 [30] and Google's LaMDA [12], has revolutionized natural language processing (NLP) and opened up new possibilities for developing advanced recommendation systems. LLMs possess the remarkable ability to generate creative text, translate languages,

Rocky5502/BUAA-DataStructures-2023-Project

Beihang University, Data Structure-2023 Project for Postgraduate International and Chinese Students.

Rocky5502/Data-Science-capstone

Capstone: Milestone Report

Rocky5502/AI-for-Medicine-Specialization

AI for Medicine Specialization - Coursera offered by deeplearning.ai

Rocky5502/Cluster-Analysis-in-Python

unsupervised learning through clustering using the SciPy library in Python.

Rocky5502/datacamp_data_scientist_with_python_career_track

This repo includes the course notes for "Data Scientist with Python"

Rocky5502/Datacamp_DataScienceTrack_Python

datacamp track: Data Scientist with Python (2019) Code & Data

Rocky5502/datasicencecoursera

Data science

Rocky5502/DataViz

Data Visualization With Matplotlib and Seaborn

Rocky5502/Does-the-Recommendation-Large-Language-Model-Balance-Fairness-and-Personality-

CCS Concepts: •Information systems → Recommender system.

Rocky5502/JHU-Data-Science-Capstone

Coursera Data Science Specialization

Rocky5502/portfolio

Rocky5502/pygram

An instagram clone

Rocky5502/pygramRocky5502

An instagram clone

Rocky5502/Resume-project

Rocky5502/Rocky2023

Rocky5502/skills-copilot-codespaces-vscode

My clone repository