In the rapidly evolving domain of cryptocurrency trading, accurate market data analysis is crucial for informed decision making. Candlestick patterns, a cornerstone of technical analysis, serve as visual representations of market sentiment and potential price movements. However, the sheer volume and complexity of cryptocurrency price time-series data presents a significant challenge to traders and analysts alike. This paper introduces an innovative rule-based methodology for recognizing candlestick patterns in cryptocurrency markets using Python. By focusing on Ethereum, Bitcoin, and Litecoin, this study demonstrates the effectiveness of the proposed methodology in identifying key candlestick patterns associated with significant market movements. The structured approach simplifies the recognition process while enhancing the precision and reliability of market analysis. Through rigorous testing, this study shows that the automated recognition of these patterns provides actionable insights for traders. This paper concludes with a discussion on the implications, limitations, and potential future research directions that contribute to the field of computational finance by offering a novel tool for automated analysis in the highly volatile cryptocurrency market.

This study presents a potential methodology for the identification of individuals based on their gait patterns, even when they are wearing different attire. The approach described in this study utilizes modified local patterns that have been specifically designed to be robust to rotation. These patterns enable the extraction of more discerning characteristics, particularly in scenarios where individuals may be wearing diverse clothing. The procedure was outlined in a comprehensive manner, including explanations for each step of the process. The experiments provided confirmation of the notable efficiency of gait-based person identification in scenarios where individuals wear varying attire. Keywords: Surveillance systems, Human identification, Silhouette extraction, Gait Energy Image, classification, Local Binary Pattern, Gait recognition.