17 skills found
roboflow / Rf Detr[ICLR 2026] RF-DETR is a real-time object detection and segmentation model architecture developed by Roboflow, SOTA on COCO, designed for fine-tuning.
MKme / MasterFrequencyListErics Master Frequency lists for RF tuning
Roboflow-Universe / Finetune RF DETRModular CLI pipeline for fine‑tuning RF‑DETR object detection models on custom datasets.
al1ce23 / Wave BubbleMirror of self-tuning portable RF jammer
chriskacerguis / Honeywell2mqttA Docker image for a software defined radio tuned to listen for Honeywell RF security sensors at 345Mhz
al1ce23 / Wave Bubble RC1aSchematics & Firmware for self-tuning portable RF Jammer
Aayushi-2808 / Cervical Cancer Detection Using ML# Cervical_cancer_detection_using_ML # Introduction According to World Health Organisation (WHO), when detected at an early stage, cervical cancer is one of the most curable cancers. Hence, the main motive behind this project is to detect the cancer in its early stages so that it can be treated and managed in the patients effectively. # Flow of project is as explained below: This project is divided into 5 parts: 1. Data Cleaning 2. Exploratory Data Analysis 3. Baseline model: Logistic Regression 4. Ensemble Models: Bagging with Decision Trees, Random forest and Boosting 5. Model Comparison and results # Refer below for References: Link to basic information regarding cervical cancer : https://www.cdc.gov/cancer/cervical/basic_info/index.htm The dataset for tackling the problem is supplied by the UCI repository for Machine Learning. Link to Dataset : https://archive.ics.uci.edu/ml/datasets/Cervical+cancer+%28Risk+Factors%29 The dataset contains a list of risk factors that lead up to the Biopsy examination. The generation of the predictor variable is taken care of in part 2 (Exploratory data analysis) of this report. We will try to predict the 'biopsy' variable from the dataset using Logistic Regression, Random Forest, Bagging with Decision Trees and Boosting with XGBoost Classifier. # Results: Based on our Base model and The Ensemble Models we used, we observed - 1. After the entire process of training, hyperparameter tuning and tackling class imbalance was complete , we obtained the results as depicted through the graphics. 2. We observe that Bagging and Random Forest gives the highest accuracy and precision of 97.09 and 80% resp. 3. Plotting the Confusion matrix showed us that Random Forest using upsampling and class weights gives us 2 false positives and 3 false negatives with auc of 0.87 # Why random forest is the best model?? 1. So as we see, while comparing all of our models,RF has maximum f1_score and accuracy along with Bagging i.e. 76.2 n 97.09% resp. 2. And it also produces the same amount of false negatives with a recall of 72.73% just like all the other models. 3. But we still consider RF better coz of its added advantage that, the decision trees are decorrelated as compared to bagging leading to lesser variance and greater ability to generalize. # Conclusion: On observing the feature importance of the best model i.e random forest, we can see that the most important features are Schiller, Hinselmann, HPV, Citology, etc. This also makes sense because Schiller and Hinselmann are actually the tests used to detect cervical cancer. # Problems Faced: A major problem encountered while training the model was that it had too little data to train. On collaborating with all the hospitals in India, we can have enough data points to train a model with a higher recall, thus making the model better. # Scope of Improvement As next steps I would want to do exactly that, to deploy the model and refine it. We may also modify the number of the predictor variables, as it may well turn out that there are other predictors which may not be present in our current dataset. This can only be found by practical implementation of our predictions.
cpyarger / Home Assistant AddonsA hass.io addon for a software defined radio tuned to listen for Utility Meter RF transmissions and republish the data via Home Assistant's API
vishnupriyanpr / PrediChurnA full ML pipeline for customer churn prediction in telecom, banking, or SaaS. Includes robust data cleaning, automatic feature engineering, model training/tuning (Logistic Regression, RF, XGBoost), interpretability, and interactive dashboards for actionable business retention insights.
gerritjandebruin / Ha RtlA Home Assistant addon for a software defined radio tuned to listen for RF transmissions and convert them into device triggers. This device trigger can be used to trigger any automation as soon as a specific message is received. For example, the add-on allows to integrate the KAKU ACDB-7000A to be used in Home Assistant. Setup is done automatically via MQTT Discovery.
tmc9031 / RF Tuning ToolA program to facilitate RF tuning
PierreMarieCurie / Train Rf Detr OIDv7One‑line fine‑tuning of RF‑DETR on selected classes from OpenImages V7
josalggui / RFAutoMaTEPCB for automatic tuning matching of RF coil
mehrdaddaviran / Tuning RF And SVM Parms With PSODevelopment of a novel approach: Combine PSO with Random Forest and Support Vector Machines called (PSO-SVM&PSO-RF) to optimize Artificial Intelligence-based Mineral Prospectivity Mapping (AI-MPM)
roryclear / Rf Detr Tinygrad[ICLR 2026] RF-DETR is a real-time object detection and segmentation model architecture developed by Roboflow, SOTA on COCO, designed for fine-tuning.
ohmthanap / Telecom Customer Churn PredictionsDeveloped a churn prediction classification model using various techniques including: EDA, Decision trees, Naive Bayes, AdaBoost, MLP, Bagging, RF, KNN, logistic regression, SVM, Hyperparameter tuning using Grid Search CV and Randomized Search CV.
kamruleee51 / Diabetes Classification DatasetIn this article, we proposed a new labeled diabetes dataset from a South Asian country (Bangladesh). Additionally, we recommended an automated classification pipeline, introducing a weighted ensemble of several Machine Learning (ML) classifiers: Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), XGBoost (XGB), and LightGBM (LGB). The critical hyperparameters of these ML models are tuned using a grid search hyperparameter optimization approach. Missing values imputation, feature selection, and K-fold cross-validation were also incorporated into the designed framework.
