AIMC Topic: Forecasting

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A hybrid model integrating long short-term memory with adaptive genetic algorithm based on individual ranking for stock index prediction.

PloS one
Modeling and forecasting stock prices have been important financial research topics in academia. This study seeks to determine whether improvements can be achieved by forecasting a stock index using a hybrid model and incorporating financial variable...

Investigation of a Data Split Strategy Involving the Time Axis in Adverse Event Prediction Using Machine Learning.

Journal of chemical information and modeling
Adverse events are a serious issue in drug development, and many prediction methods using machine learning have been developed. The random split cross-validation is the de facto standard for model building and evaluation in machine learning, but care...

Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting Models.

Sensors (Basel, Switzerland)
To improve the monitoring of the electrical power grid, it is necessary to evaluate the influence of contamination in relation to leakage current and its progression to a disruptive discharge. In this paper, insulators were tested in a saline chamber...

Application and Prospect Analysis of Artificial Intelligence in the Field of Physical Education.

Computational intelligence and neuroscience
The development of AI technology has a significant impact on every sector of business. Artificial intelligence uses this technology to reduce the amount of work required, duplicate work, and increase the accuracy of work by modelling human behaviour ...

Sparrow Search Algorithm-Optimized Long Short-Term Memory Model for Stock Trend Prediction.

Computational intelligence and neuroscience
The long short-term memory (LSTM) network is especially suitable for dealing with time series-related problems, which has led to a wide range of applications in analyzing stock market quotations and predicting future price trends. However, the select...

The role of deep learning in urban water management: A critical review.

Water research
Deep learning techniques and algorithms are emerging as a disruptive technology with the potential to transform global economies, environments and societies. They have been applied to planning and management problems of urban water systems in general...

Reducing Detrimental Communication Failure Impacts in Microgrids by Using Deep Learning Techniques.

Sensors (Basel, Switzerland)
A Microgrid (MG), like any other smart and interoperable power system, requires device-to-device (D2D) communication structures in order to function effectively. This communication system, however, is not immune to intentional or unintentional failur...

Nested Bee Hive: A Conceptual Multilayer Architecture for 6G in Futuristic Sustainable Smart Cities.

Sensors (Basel, Switzerland)
Several smart city ideas are introduced to manage various problems caused by overpopulation, but the futuristic smart city is a concept based on dense and artificial-intelligence-centric cities. Thus, massive device connectivity with huge data traffi...

A novel approach for COVID-19 Infection forecasting based on multi-source deep transfer learning.

Computers in biology and medicine
COVID-19 is a contagious disease; so, predicting its future infections in a provincial region requires the consideration of the related data (i.e., rates of infection, mortality and recovery, etc.) over a period of time. Clearly, the COVID-19 data of...