Professional programmer with experience in machine learning and smart contract development using Solidity. Skilled in applying advanced machine learning algorithms and implementing them in various projects. Proven expertise in designing and deploying smart contracts for blockchain platforms and working with decentralized networks.
1. [Dark Web Activity Classification Using Deep Learning](Link to the Paper)
- Abstract:In contemporary times, people rely heavily on the internet and search engines to obtain information, either directly or indirectly. However, the information accessible to users constitutes merely 4% of the overall information present on the internet, which is commonly known as the surface web. The remaining information that eludes search engines is called the deep web. The deep web encompasses deliberately hidden information, such as personal email accounts, social media accounts, online banking accounts, and other confidential data. The deep web contains several critical applications, including databases of universities, banks, and civil records, which are off-limits and illegal to access. The dark web is a subset of the deep web that provides an ideal platform for criminals and smugglers to engage in illicit activities, such as drug trafficking, weapon smuggling, selling stolen bank cards, and money laundering. In this article, we propose a search engine that employs deep learning to detect the titles of activities on the dark web. We focus on five categories of activities, including drug trading, weapon trading, selling stolen bank cards, selling fake IDs, and selling illegal currencies. Our aim is to extract relevant images from websites with a ".onion" extension and identify the titles of websites without images by extracting keywords from the text of the pages. Furthermore, we introduce a dataset of images called Darkoob, which we have gathered and used to evaluate our proposed method. Our experimental results demonstrate that the proposed method achieves an accuracy rate of 94% on the test dataset.
- Publication Year: 2023/6/25
- Link to the Paper:Link to the Paper
2. [Introducing A Novel Method For Adaptive Thresholding In Brain Tumor Medical Image Segmentation](Link to the Paper)
- Abstract: 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.
- Publication Year: 2023/5/30
- Link to the Paper:Link to the Paper
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