Tag: 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.

  • 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…

  • Dabl Auto EDA-ML

    Dabl Auto EDA-ML

    Dabl, short for Data Analysis Baseline Library, is a high-level data exploration library in Python that automates repetitive data wrangling tasks in the early stages of supervised machine learning model development. Developed by Andreas Mueller and the scikit-learn community, it facilitates data preprocessing, advanced integrated visualization, exploratory data analysis (EDA), and ML model development, demonstrated…

  • Multi-Label Keras CNN Image Classification of MNIST Fashion Clothing

    Multi-Label Keras CNN Image Classification of MNIST Fashion Clothing

    Machine learning and deep learning are invaluable in optimizing supply chain operations in fashion retail. Even smaller retailers are leveraging ML algorithms to meet customer demands. Neural network models, particularly Convolution Neural Networks (CNN) are used to classify clothing images, like the Fashion-MNIST dataset, with high accuracy. Hyperparameter optimization using GridSearchCV and Nadam optimizer are…

  • AI-Based ECG Recognition – EOY ’22 Status

    AI-Based ECG Recognition – EOY ’22 Status

    Featured Photo by cottonbro studio on pexels. Electrocardiography (ECG) is the method most often used to diagnose cardiovascular diseases. The recent study demonstrates that an AI is capable of automatically diagnosing the abnormalities indicated by an ECG. In this post we will review and illustrate how AI applies to ECG analysis to outperform traditional ECG analysis.…

  • 99% Accurate Breast Cancer Classification using Neural Networks in TensorFlow

    99% Accurate Breast Cancer Classification using Neural Networks in TensorFlow

    Breast cancer is a significant global health concern, affecting 12% of women. Machine Learning and Artificial Intelligence techniques play a crucial role in early diagnosis using image features. The study demonstrates a successful Neural Network model for breast cancer classification, achieving 98% accuracy and 98% F1-score. Multiple metrics confirm the model’s efficiency.

  • The Power of AIHealth: Comparison of 12 ML Breast Cancer Classification Models

    The Power of AIHealth: Comparison of 12 ML Breast Cancer Classification Models

    AI Health is leveraging Machine Learning (ML) and Artificial Intelligence (AI) for early diagnosis and prediction of breast cancer (BC), utilizing different ML techniques for binary classification of the disease. A comparative analysis demonstrated that Linear Regression was the most effective classifier based on various performance metrics. This research aims to integrate ML in public…

  • DL-Assisted ECG/EKG Anomaly Detection using LSTM Autoencoder

    DL-Assisted ECG/EKG Anomaly Detection using LSTM Autoencoder

    This project implements an ECG anomaly detection framework using an LSTM Autoencoder to accurately identify abnormal ECG events. It trains the autoencoder on normal rhythms, using reconstruction errors to identify anomalies. The proposed method aims to improve abnormal ECG detection, as demonstrated by test results on the ECG5000 dataset, providing valuable information for patient health…

  • ML/AI Breast Cancer Diagnosis with 98% Confidence

    ML/AI Breast Cancer Diagnosis with 98% Confidence

    We demonstrate the importance of hyperparameter optimization (HPO) for enhancing ML prediction accuracy. Specifically, we will focus on the Random Forest Classifier (RFC) as an ensemble of decision trees. RFC is a supervised ML algorithm that has been applied successfully to the BC binary classification. 

  • Breast Cancer ML Classification – Logistic Regression vs Gradient Boosting with Hyperparameter Optimization (HPO)

    Breast Cancer ML Classification – Logistic Regression vs Gradient Boosting with Hyperparameter Optimization (HPO)

    Breast Cancer (BC) is the leading cause of death among women worldwide. The present study optimizes the use of supervised Machine Learning (ML) algorithms for detecting, analyzing, and classifying BC. We compare Logistic Regression (LR) against Gradient Boosting (GB) Classifier within the Hyperparameter Optimization (HPO) loop given by GridSearchCV. We use the publicly available BC dataset…

  • A Comparative Analysis of Breast Cancer ML/AI Binary Classifications

    A Comparative Analysis of Breast Cancer ML/AI Binary Classifications

    This study is dedicated to #BreastCancerAwarenessMonth2022 #breastcancer #BreastCancerDay @Breastcancerorg @BCAction @BCRFcure @NBCF @LivingBeyondBC @breastcancer @TheBreastCancer @thepinkribbon @BreastCancerNow. One of the most common cancer types is breast cancer (BC), and early diagnosis is the most important thing in its treatment. Recent studies have shown that BC can be accurately predicted and diagnosed using machine learning (ML) technology. Our…

  • HealthTech ML/AI Q3 ’22 Round-Up

    HealthTech ML/AI Q3 ’22 Round-Up

    Featured Photo by Andy Kelly on Unsplash This blog presents a Q3 ’22 summary of current healthtech ML/AI innovation methods, trends and challenges. Virtual reality, artificial intelligence, augmented reality, and machine learning are all healthcare technology trends that are going to play a vital role across the entire healthcare system. Let’s take a look at…

  • AI-Powered Stroke Prediction

    Contents: Introduction Stroke, also known as brain attack, happens when blood flow to the brain is blocked, preventing it from getting oxygen and nutrients from it and causing the death of brain cells within minutes. According to the World Health Organization (WHO) stroke is the 2nd leading cause of death globally after ischemic heart disease,…

  • E-Commerce ML/AI Classification

    The article outlines the process of using TensorFlow to categorize clothing images. It involves pre-processing data, building an ML model, and making predictions. The application relies on Python and Jupyter Anaconda IDE, using the tf.keras high-level API. The dataset comprises 60,000 grayscale images in various fashion categories, with data divided into training and testing sets…

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

    Pilots Related to HealthTech Infographic These plots illustrate the most basic application of ML/AI in BCD as the binary classification problem. Classification usually refers to any kind of problem where a specific type of class label is the result to be predicted from the given input field of data. This is a task which assigns a label value…

  • Python Use-Case Supervised ML/AI in Breast Cancer (BC) Classification

      https://www.canva.com/design/DAE7oU6O6QQ/share/preview?token=xH-OB2oXeQSrennmqMC2hw&role=EDITOR&utm_content=DAE7oU6O6QQ&utm_campaign=designshare&utm_medium=link&utm_source=sharebutton Acknowledgements with the ML/AI contribution https://hiscidatmlai.blogspot.com/2022/02/digital-transformation-all-way.html… and @VismeApp #Graphics via ref https://visme.co/?ref=al24 Thanks to Mugdha Paithankar [1] and https://kaggle.com/uciml/breast-cancer-wisconsin-data… [2] for the shared open-source content! Introduction Breast Cancer (BC) continues to be the most frequent cancer in females, affecting about one in 8 women and causing the highest number of cancer-related deaths in…