Tag: binary classification

  • 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.

  • Health Insurance Cross Sell Prediction with ML Model Tuning & Validation

    Health Insurance Cross Sell Prediction with ML Model Tuning & Validation

    The content discusses the use of AI and Machine Learning (ML) for insurance cross-selling. It covers topics such as data preparation, model training with different algorithms, parameter optimization, and model evaluation. The study showcases the ability of ML models (HGBM, XGBoost, Random Forest) to predict cross-sell customers in the insurance sector, providing potential for improved…

  • 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…

  • Comparison of 20 ML + NLP Algorithms for SMS Spam-Ham Binary Classification

    Comparison of 20 ML + NLP Algorithms for SMS Spam-Ham Binary Classification

    This post analyzes a public-domain SMS text message dataset to compare various machine learning algorithms’ abilities to classify spam and ham messages. After implementing a Python workflow that includes data preparation, exploratory analysis, natural language processing, supervised machine learning binary classification, and a model performance analysis, the author finds that MLP, Logistic Regression CV, Linear…

  • Improved Multiple-Model ML/DL Credit Card Fraud Detection: F1=88% & ROC=91%

    Improved Multiple-Model ML/DL Credit Card Fraud Detection: F1=88% & ROC=91%

    In 2023, the global card industry is projected to suffer $36.13 billion in fraud losses. This has necessitated a priority focus on enhancing credit card fraud detection by banks and financial organizations. AI-based techniques are making fraud detection easier and more accurate, with models able to recognize unusual transactions and fraud. The post discusses a…

  • Early Heart Attack Prediction using ECG Autoencoder and 19 ML/AI Models with Test Performance QC Comparisons

    Early Heart Attack Prediction using ECG Autoencoder and 19 ML/AI Models with Test Performance QC Comparisons

    Table of Contents Embed Socials: ECG Autoencoder Let’s set the working directory YOURPATH import osos.chdir(‘YOURPATH’)os. getcwd() and import the following libraries import tensorflow as tfimport matplotlib.pyplot as pltimport numpy as npimport pandas as pd from tensorflow.keras import layers, lossesfrom sklearn.model_selection import train_test_splitfrom tensorflow.keras.models import Model Let’s read the input dataset df = pd.read_csv(‘ecg.csv’, header=None) Let’s…

  • Dealing with Imbalanced Data in HealthTech ML/AI – 1. Stroke Prediction

    Dealing with Imbalanced Data in HealthTech ML/AI – 1. Stroke Prediction

    This post discusses the prediction of stroke using machine learning (ML) models, focusing on the use of early warning systems and data balancing techniques to manage the highly imbalanced stroke data. It includes a detailed exploration of the torch artificial neural network training and performance evaluation, as well as the implementation and evaluation of various…

  • A Comparison of Scikit Learn Algorithms for Breast Cancer Classification – 2. Cross Validation vs Performance

    A Comparison of Scikit Learn Algorithms for Breast Cancer Classification – 2. Cross Validation vs Performance

    The post is a continuation of a previous breast cancer study comparing Scikit-Learn binary classifiers for cross validation and model performance. The classifiers compared include Logistic Regression, GaussianNB, SVC, KNN, Random Forest, Extra Trees, and Gradient Boosting. Learning curves show the comparison of classifier performance. Results indicate GaussianNB is more efficient than SVC in terms…

  • ML/AI Image Classifier for Skin Cancer Detection

      Skin cancer is one of the most active types of cancer in the present decade. As the skin is the body’s largest organ, the point of considering skin cancer as the most common type of cancer among humans is understandable. It is generally classified into two major categories: nonmelanoma (benign) and melanoma (malignant) skin cancer…

  • Supervised ML/AI Breast Cancer Diagnostics – The Power of HealthTech

      Problem Breast cancer (BC) is the uncontrollable growth of malignant cells in the breasts [1]. BC is the most common cancer with the highest mortality rate. The exact cause of breast cancer is unknown, but some women have a higher risk than others. This includes women with a personal or family history of breast cancer and…