TradeslyPRO Desktop

TradeslyPRO Desktop is most feature rich robo-advisor of mine: generate signals with advanced machine learning algorithms, backtest them properly with historical data, determines market / instrument regimes, automated techincal analysis, price action analysis, custom developed Tradesly© Expected Excess Return Rank Indicator for stock performances, portfolio optimization / construction, and more. Please contact for licensing.

TradeslyPRO Cryptocurrency AI Robo-Advisor

TradeslyPRO is a cryptocurrency trading app that uses artificial intelligence and machine learning to offer you the best stock tracker and robo-advisor features. It helps you track prices, discover opportunities, and invest in cryptocurrencies. TradeslyPRO is designed for traders who want to trade safer and easier in the cryptocurrency market. Contains more features than TradeslyFX: Generates new alphas with genetic algorithms and backtests them with historical data, determines market status and prices anomalies, and more.

TradeslyFX Forex AI Robo-Advisor

TradeslyFX is a forex trading app that uses machine learning and data analytics to provide you with the best stock tracker and robo-advisor features. It helps you monitor the market, analyze trends, and execute trades. TradeslyFX is designed for traders who want to trade smarter and faster in the forex market.

AutoML App Suite

AutoML App Suite: AutoML App Suite is a collection of one-click tools that automate various data science tasks. AutoTS handles time series modeling and forecasting with state-of-the-art algorithms. AutoAD detects anomalies in univariate and multivariate datasets using advanced algorithms. AutoCS calculates, models, and generates customer success metrics from invoice data. AutoML App Suite simplifies data science for you. Please contact for licensing.

Pre-Covid and Post-Covid Twitter sentiment analysis

The main objective of this project is to examine the sentiment on Twitter during two distinct periods: before and after the Covid-19 outbreak. By utilizing advanced techniques in natural language processing, we delve into a large volume of Twitter data to extract meaningful findings regarding public sentiment and its changes over time. Our analysis aims to comprehend the shifts in attitudes, emotions, and opinions expressed on social media prior to and following the Covid-19 pandemic. By illuminating these sentiments, we gain a deeper comprehension of the social and emotional consequences of the global crisis, enabling informed decision-making and proactive responses in future situations.

Image Forgery Localization with Deep Learning

This focuses on image forgery localization using deep learning techniques and the generation of masks based on the predicted results. With the rise of digital manipulation and the proliferation of fake images, it has become crucial to develop robust methods for detecting and localizing forged regions within images. By leveraging the power of deep learning algorithms, we have trained a model to accurately identify areas that have been tampered with. Furthermore, we have developed a mechanism to generate masks that precisely outline the forged regions, visually representing the detected manipulations. Our project aims to contribute to the fight against image forgery by providing an efficient and reliable tool for forensic analysis and verification of authenticity.

Text summarization with PageRank

The objective of this project is to improve the text summarization process through the combination of the cosine distance and PageRank algorithms. By using the cosine distance, we assess the semantic similarities among sentences, ensuring that the summary effectively captures the essential meaning of the original text. Additionally, by incorporating the PageRank algorithm, we assign importance scores to sentences based on their centrality, giving priority to crucial information in the summary. This approach enables users to efficiently understand and digest extensive amounts of text by generating concise and coherent summaries. The ultimate goal of this project is to enhance productivity and accessibility across various industries, making information more manageable and actionable.

Natural Language Processing

Python

NLTK

Website Clustering and Classification with ML

This project focuses on clustering webpages using word vectors and classifying new pages based on their content. By leveraging advanced natural language processing techniques, we convert webpage text into word vectors, which capture the semantic meaning and relationships between words. Through clustering algorithms, we group similar webpages together based on their word vector representations, enabling the efficient organization and navigation of related content. Additionally, we develop a classification model trained on the clustered data, allowing us to classify new webpages into their respective clusters or categories. This project aims to improve information retrieval and categorization systems, facilitating better content management and enhancing the user experience in web browsing.

Natural Language Processing

Multi-class weather recognition from still image with Deep Learning

This project focuses on the development of a deep-learning model for multi-class weather recognition from still images. By utilizing advanced convolutional neural networks (CNNs) and image processing techniques, we have created an accurate and efficient system capable of categorizing images into various weather conditions. This technology has numerous applications, ranging from automated weather monitoring to enhancing image search and retrieval systems. Our project aims to provide a valuable tool for weather analysis and enable more informed decision-making in various industries that rely on weather information.