Tootfinder

Hybrid Approach for Electricity Price Forecasting using AlexNet and LSTM
Bosubabu Sambana, Kotamsetty Geethika Devi, Bandi Rajeswara Reddy, Galeti Mohammad Hussain, Gownivalla Siddartha
https://arxiv.org/abs/2506.23504

Hybrid Approach for Electricity Price Forecasting using AlexNet and LSTM
The recent development of advanced machine learning methods for hybrid models has greatly addressed the need for the correct prediction of electrical prices. This method combines AlexNet and LSTM algorithms, which are used to introduce a new model with higher accuracy in price forecasting. Despite RNN and ANN being effective, they often fail to deal with forex time sequence data. The traditional methods do not accurately forecast the prices. These traditional methods only focus on demand and pr…

Persistence Paradox in Dynamic Science
Honglin Bao, Kai Li
https://arxiv.org/abs/2506.22729 https://arxiv.org/pdf/2506.22729

Persistence Paradox in Dynamic Science
Persistence is often regarded as a virtue in science. In this paper, however, we challenge this conventional view by highlighting its contextual nature, particularly how persistence can become a liability during periods of paradigm shift. We focus on the deep learning revolution catalyzed by AlexNet in 2012. Analyzing the 20-year career trajectories of over 5,000 scientists who were active in top machine learning venues during the preceding decade, we examine how their research focus and output…