首页|Findings from Chitkara University in the Area of Artificial Intelligence Describ ed (An Adaptive Salp-stochastic-gradient-descentbased Convolutional Lstm With M apreduce Framework for the Prediction of Rainfall)
Findings from Chitkara University in the Area of Artificial Intelligence Describ ed (An Adaptive Salp-stochastic-gradient-descentbased Convolutional Lstm With M apreduce Framework for the Prediction of Rainfall)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing-Artificial Intelligence have been published. According to news originating from Himachal Prades, India, by NewsRx correspondents, research stated, "Rainfa ll prediction is considered to be an esteemed research area that impacts the day -to-day life of Indians. The predominant income source of most of the Indian pop ulation is agriculture." Our news journalists obtained a quote from the research from Chitkara University, "It helps the farmers to make the appropriate decisions pertaining to cultivat ion and irrigation. The primary objective of this investigation is to develop a technique for rainfall prediction utilising the MapReduce framework and the conv olutional long short-term memory (ConvLSTM) method to circumvent the limitations of higher computational requirements and the inability to process a large numbe r of data points. In this work, an adaptive salp-stochastic-gradientdescent-base d ConvLSTM (adaptive S-SGD-based ConvLSTM) system has been developed to predict rainfall accurately to process the long time series data and to eliminate the va nishing problems. To optimize the hyperparameter of the convLSTM model, the S-SG D methodology proposed combine the SGD and the salp swarm algorithm (SSA). The a daptive S-SGD based ConvLSTM has been developed by integrating the adaptive conc ept in S-SGD. It tunes the weights of ConvLSTM optimally to achieve better predi ction accuracy. Assessment measures, such as the percentage root mean square dif ference (PRD) and mean square error (MSE), were employed to compare the suggeste d method with previous approaches."
Himachal PradesIndiaAsiaArtificial IntelligenceMachine LearningChitkara University
NETL
NSTL
