The importance of predicting rainfall in fields that require information such as agriculture, transportation and industry. Prediction with statistics is done to solve the problems this paper, thus paper proposes prediction using Long Short Term Memory case study: Surabaya City. data used at two stations, namely Perak Meteorological Station I Tanjung Maritime Meteorology from 2015 2020. test was...

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-toend deep dynamic neural network that reads medical records, stores previous illness history, infers current ill...

Recently, the long short-term memory neural network (LSTM) has attracted wide interest due to its success in many tasks. LSTM architecture consists of a memory cell and three gates, which looks similar to the neuronal networks in the brain. However, there still lacks the evidence of the cognitive plausibility of LSTM architecture as well as its working mechanism. In this paper, we study the cog...

HTM-MAT is a MATLAB® based toolbox for implementing cortical learning algorithms (CLA) including related cortical-like algorithms that possesses spatiotemporal properties. CLA is a suite of predictive machine learning algorithms developed by Numenta Inc. and is based on the hierarchical temporal memory (HTM). This paper presents an implementation of HTM-MAT with several illustrative examples in...

The idea of using metaplastic synapses to incorporate the separate storage of longand short-term memories via an array of hidden states was put forward in the cascade model of Fusi et al. In this paper, we devise and investigate two models of a metaplastic synapse based on these general principles. The main difference between the two models lies in their available mechanisms of decay, when a co...