Filtron: A Learning-Based Anti-Spam Filter

We present Filtron, a prototype anti-spam filter that integrates the main empirical conclusions of our comprehensive analysis on using machine learning to construct e#ective personalized anti-spam filters. Filtron is based on the experimental results over several design parameters on four publicly available benchmark corpora. After describing Filtron's architecture, we assess its behavior in real use over a period of seven months. The results are deemed satisfactory, though they can be improved with more elaborate preprocessing and regular re-training

AN ENSEMBLE TEMPLATE MATCHING AND CONTENT-BASED IMAGE RETRIEVAL SCHEME TOWARDS EARLY STAGE DETECTION OF MELANOMA

Malignant melanoma represents the most dangerous type of skin cancer. In this study we present an ensemble classification scheme, employing the mutual information, the cross-correlation and the clustering based on proximity of image features methods, for early stage assessment of melanomas on plain photography images. The proposed scheme performs two main operations. First, it retrieves the most similar, to the unknown case, image samples from an available image database with verified benign moles and malignant melanoma cases. Second, it provides an automated estimation regarding the nature of the unknown image sample based on the majority of the most similar images retrieved from the available database. Clinical material comprised 75 melanoma and 75 benign plain photography images collected from publicly available dermatological atlases. Results showed that the ensemble scheme outperformed all other methods tested in terms of accuracy with 94.9±1.5%, following an external cross-validation evaluation methodology. The proposed scheme may benefit patients by providing a second opinion consultation during the self-skin examination process and the physician by providing a second opinion estimation regarding the nature of suspicious moles that may assist towards decision making especially for ambiguous cases, safeguarding, in this way from potential diagnostic misinterpretations

holoviz/spatialpandas: Version 0.4.9

This is a compatibility release to support Pandas 2.1. Compatibility: Support Pandas 2.1 (#125) Don't compare empty geometry tests against geopandas (#127) Enhancements: Rename pyviz-dev as holoviz-dev (#120) Use holoviz_tasks/install action for CI (#123) Thanks to @maximlt, @Hoxbro and @ianthomas23