Cargo loss/damage is a very common problem faced by almost any business with a supply chain arm, leading to major problems like revenue loss and reputation tarnishing. This problem can be solved by employing an asset and impact tracking solution. This would be more practical and effective for high-cost cargo in comparison to low-cost cargo due to the high costs associated with the sensors and overall solution. In this study, we propose a low-cost solution architecture that is scalable, user-friendly, easy to adopt and is viable for a large range of cargo and logistics systems. Taking inspiration from a real-life use case we solved for a client, we also provide insights into the architecture as well as the design decisions that make this a reality.
Machine learning driven trading strategies have garnered a lot of interest over the past few years. There is, however, limited consensus on the ideal approach for the development of such trading strategies. Further, most literature has focused on trading strategies for short-term trading, with little or no focus on strategies that attempt to build longterm wealth. Our paper proposes a new approach for developing long-term investment strategies using an ensemble of evolutionary algorithms and a deep learning model by taking a series of shortterm purchase decisions. Our methodology focuses on building long-term wealth by improving systematic investment planning (SIP) decisions on Exchange Traded Funds (ETF) over a period of time. We provide empirical evidence of superior performance (around 1% higher returns) using our ensemble approach as compared to the traditional daily systematic investment practice on a given ETF. Our results are based on live trading decisions made by our algorithm and executed on the Robinhood trading platform.
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