Category: Artificial Intelligence

  • Time Series Data Imputation, Interpolation & Anomaly Detection

    Time Series Data Imputation, Interpolation & Anomaly Detection

    The post compares popular time series data imputation, interpolation, and anomaly detection methods. It explores the challenges of missing data and the impact on processing, analyzing, and model accuracy. The study performs data-centric experiments to benchmark optimal methods and highlights the importance of imputation for time series forecasting. It provides practical strategies and techniques for…

  • Kalman-Based Object Tracking with Low Signal/Noise Ratio

    Kalman-Based Object Tracking with Low Signal/Noise Ratio

    This study focuses on real-time object tracking with low signal/noise ratios using Kalman Filter (KF) algorithms. The study covers 1D, 2D, and 3D motion analysis, and explores the impact of noise on the accuracy of object tracking. The accuracy of the KF algorithms in estimating the object’s position and speed in real-time scenarios is evaluated…

  • MLflow SHAP & Transformers

    MLflow SHAP & Transformers

    The post covers simplified MLflow projects for reproducible and reusable data science code. It details local environment setup, ElasticNet model optimization, and SHAP explanations for breast cancer, diabetes, and iris datasets. Additionally, it showcases MLflow Sentence Transformers for a chatbot and translation. This demonstrates their powerful interface for managing transformer models from libraries like Hugging…

  • Malware Detection & Interpretation – PCA, T-SNE & ML

    Malware Detection & Interpretation – PCA, T-SNE & ML

    This post discusses the application of PCA, T-SNE, and supervised ML algorithms for malware detection using a benchmark dataset. Techniques such as Logistic Regression, SVC, KNN, and XGBoost are implemented, achieving high performance metrics. Results show potential for improving malware detection using ML while reducing false positives and enhancing cyber defense.

  • H2O AutoML Malware Detection

    H2O AutoML Malware Detection

    This study explores AI-powered malware detection using the H2O AutoML algorithm for effective and rapid classification of PE files. The optimized Stacked Ensemble model achieved high precision, recall, and F1 score. The research validates the H2O AutoML workflow’s accurate malware identification and supports related R&D products and solutions in the field of information security.

  • A Market-Neutral Strategy

    A Market-Neutral Strategy

    The work aims to solve the problem of Markowitz portfolio optimization for a one-year investment horizon through the pairs trading cointegrated strategy. Market-neutral trading strategies seek to generate returns independent of market swings to achieve a zero beta against its relevant market index. Statistical arbitrage (SA), pairs trading, and APO signals are analyzed. The study…

  • Sales Forecasting: tslearn, Random Walk, Holt-Winters, SARIMAX, GARCH, Prophet, and LSTM

    Sales Forecasting: tslearn, Random Walk, Holt-Winters, SARIMAX, GARCH, Prophet, and LSTM

    The data science project involves evaluating various sales forecasting algorithms in Python using a Kaggle time-series dataset. The forecasting algorithms include tslearn, Random Walk, Holt-Winters, SARIMA, GARCH, Prophet, LSTM and Di Pietro’s Model. The goal is to predict next month’s sales for a list of shops and products, which slightly changes every month. The best…

  • Prediction of NASA Turbofan Jet Engine RUL: OLS, SciKit-Learn & LSTM

    Prediction of NASA Turbofan Jet Engine RUL: OLS, SciKit-Learn & LSTM

    We predict the Remaining Useful Life (RUL) of NASA turbofan jet engines by comparing the statsmodels OLS, ML SciKit-Learn regression vs LSTM Keras in Python. The input dataset is the Kaggle version of the public dataset for asset degradation modeling from NASA. It includes Run-to-Failure simulated data from turbo fan jet engines.

  • The 5-Step GCP IoT Device-to-Report via AI Roadmap

    The 5-Step GCP IoT Device-to-Report via AI Roadmap

    The Internet of Things (IoT) aids in the improvement of processes and enables new scenarios through network-connected devices. Recognized as a driver of the Fourth Industrial Revolution, IoT applications include predictive maintenance, industry safety, automation, remote monitoring, asset tracking, and fraud detection. Advancements in cloud IoT architectures over recent years have enabled efficient data ingestion,…

  • Weather Forecasting & Flood De-Risking using Machine Learning, Markov Chain & Geospatial Plotly EDA

    Weather Forecasting & Flood De-Risking using Machine Learning, Markov Chain & Geospatial Plotly EDA

    Foto door Pok Rie Scope: Business Value: Table of Contents U.S.A. Weather Forecast Australian Rainfall Prediction Kerala Flood Prediction Squares are categorical associations (uncertainty coefficient & correlation ratio) from 0 to 1. The uncertainty coefficient is asymmetrical, (i.e. ROW LABEL values indicate how much they PROVIDE INFORMATION to each LABEL at the TOP). • Circles are the symmetrical numerical…

  • Hugging Face NLP, Streamlit, PyGWalker, TF & Gradio App

    Hugging Face NLP, Streamlit, PyGWalker, TF & Gradio App

    Table of Contents Streamlit/Dash/Jupyter PyGWalker EDA Demo PyGWalker and Dash — Creating a Data Visualization Dashboard In Less Than 20 Lines of Code PyGWalker Test PyGWalker Tutorial: A Tableau-Like Python Library for Interactive Data Exploration and Visualization PyGWalker: A Python Library for Visualizing Pandas Dataframes You’ll Never Walk Alone: Use Pygwalker to Visualize Data in…

  • Dividend-NG-BTC Diversify Big Tech

    Dividend-NG-BTC Diversify Big Tech

    SEO Title: Can Dividends, Natural Gas and Crypto Diversify Big Techs? Ultimately, we need to answer the following fundamental question: Can Dividend Kings, NGUSD and BTC-USD Diversify Growth Tech assets? Dividends are very popular among investors, especially those who want a steady stream of income from their investments. Some companies choose to share their profits…

  • Anomaly Detection using the Isolation Forest Algorithm

    Anomaly Detection using the Isolation Forest Algorithm

    The post describes the application of Isolation Forest, an unsupervised anomaly detection algorithm, to identify abnormal patterns in financial and taxi ride data. The challenge is to accurately distinguish normal and abnormal data points for fraud detection, fault diagnosis, and outlier identification. Using real-world datasets of financial transactions and NYC taxi rides, the algorithm successfully…

  • Returns-Volatility Domain K-Means Clustering and LSTM Anomaly Detection of S&P 500 Stocks

    Returns-Volatility Domain K-Means Clustering and LSTM Anomaly Detection of S&P 500 Stocks

    This study aims to implement and evaluate the K-means algorithm for ranking/clustering S&P 500 stocks based on average annualized return and volatility. The second goal is to detect anomalies in the best performing S&P 500 stocks using the Isolation Forest algorithm. Additionally, anomalies in the S&P 500 historical stock price time series data will be…

  • Real-Time Anomaly Detection of NAB Ambient Temperature Readings using the TensorFlow/Keras Autoencoder

    Real-Time Anomaly Detection of NAB Ambient Temperature Readings using the TensorFlow/Keras Autoencoder

    The content covers a detailed guide on implementing anomaly detection in time series data using autoencoders. The tutorial utilizes Python and real-world temperature dataset from Numenta Anomaly Benchmark (NAB). Following the Python workflow, the algorithm imports required libraries, performs anomaly detection, and visualizes anomalies. A trained autoencoder model identifies anomalies, with Precision, Recall, and F1…

  • NVIDIA Returns-Drawdowns MVA & RNN Mean Reversal Trading

    NVIDIA Returns-Drawdowns MVA & RNN Mean Reversal Trading

    The study presents a machine learning-focused analytical approach to optimize NVIDIA’s stock performance using moving average crossovers and aims at comparing the outcomes with simple RNN mean reversal trading strategies. The steps taken involve preparing the stock data, calculating moving averages and drawdowns, plotting heatmaps of returns and drawdowns, and predicting returns and cumulative returns…

  • Oracle Monte Carlo Stock Simulations

    Oracle Monte Carlo Stock Simulations

    Oracle Corporation’s significant developments in Generative AI have led to lucrative partnerships with Nvidia and Elon Musk’s xAI. Having secured contracts exceeding $4 billion for its Generation 2 Cloud designed for AI model training, Oracle’s earnings doubled in Q4 2023. Monte Carlo simulations align with Zacks Rank 3-Hold for ORCL, implying bullish potential with projected…

  • Time Series Forecasting of Hourly U.S.A. Energy Consumption – PJM East Electricity Grid

    Time Series Forecasting of Hourly U.S.A. Energy Consumption – PJM East Electricity Grid

    Table of Contents PJME Data Let’s set the working directory YOURPATH and import the following key libraries Let’s read the input csv file in our working directory Let’s plot the time series Data Preparation Output: (113926, 1, 9) (113926,) (31439, 1, 9) (31439,) LSTM TSF Let’s plot the LSTM train/test val_loss history Output: MSE: 1811223.125…

  • Image Based Fast Forest Fire Detection with TensorFlow

    Image Based Fast Forest Fire Detection with TensorFlow

    A recent study showcases the use of artificial intelligence (AI) and deep learning (DL) for efficient wildfire prediction and management. Utilizing a fast DL approach based on the TensorFlow Convolution Neural Network (CNN) algorithm, researchers trained models to distinguish between fire and non-fire images using a public-domain dataset. The implemented system predicted fires accurately and…

  • Robust Fake News Detection: NLP Algorithms for Deep Learning and Supervised ML in Python

    Robust Fake News Detection: NLP Algorithms for Deep Learning and Supervised ML in Python

    The project aims at setting up a robust system for fake news detection using Python. The system adopts a hybrid framework, leveraging Natural Language Processing (NLP) techniques to classify text-based fake vs real news. Involving exploratory data analysis, multi-model training, testing, validation, and performance metrics comparison, it assesses different Deep Learning, Supervised Machine Learning, and…