Introducing A Novel Method For Adaptive Thresholding In Brain Tumor Medical Image Segmentation

One of the most significant challenges in the field of deep learning and medical image segmentation is to determine an appropriate threshold for classifying each pixel. This threshold is a value above which the model's output is considered to belong to a specific class. Manual thresholding based on personal experience is error-prone and time-consuming, particularly for complex problems such as medical images. Traditional methods for thresholding are not effective for determining the threshold value for such problems. To tackle this challenge, automatic thresholding methods using deep learning have been proposed. However, the main issue with these methods is that they often determine the threshold value statically without considering changes in input data. Since input data can be dynamic and may change over time, threshold determination should be adaptive and consider input data and environmental conditions.

Dark web activity classification using deep learning

The present article highlights the pressing need for identifying and controlling illicit activities on the dark web. While only 4% of the information available on the internet is accessible through regular search engines, the deep web contains a plethora of information, including personal data and online accounts, that is not indexed by search engines. The dark web, which constitutes a subset of the deep web, is a notorious breeding ground for various illegal activities, such as drug trafficking, weapon sales, and money laundering. Against this backdrop, the authors propose a novel search engine that leverages deep learning to identify and extract relevant images related to illicit activities on the dark web. Specifically, the system can detect the titles of illegal activities on the dark web and retrieve pertinent images from websites with a .onion extension. The authors have collected a comprehensive dataset named darkoob and the proposed method achieves an accuracy of 94% on the test dataset. Overall, the proposed search engine represents a significant step forward in identifying and controlling illicit activities on the dark web. By contributing to internet and community security, this technology has the potential to mitigate a wide range of social, economic, and political challenges arising from illegal activities on the dark web.