Operational forecasting often requires predicting collections of related, multivariate time
series data that are high-dimensional in nature. This can be tackled by fitting a single …

Increasing global energy demands and decreasing stocks of fossil fuels have led to a
resurgence of research into energy forecasting. Artificial intelligence, explicitly time series …

As optimization problems grow increasingly complex, traditional deterministic algorithms
often struggle to address these challenges. Metaheuristic algorithms, with their flexibility and …

Autonomous vehicles have a number of open challenges, one of which is decision-making
regarding motion, particularly while operating in an environment that is both complex and …

Deep Learning and Enhanced Emissions Modeling and Deposition Prediction JCSIS
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This study proposes a prediction model considering external weather and holiday factors to
address the issue of accurately predicting urban taxi travel demand caused by complex data …

Crop Yield Estimation Using Spiking Neural Networks Through Spatiotemporal Analysis of
Image Time Series JCSIS 019928311823 info@jcsis.org JCSIS About Volumes Instructions …

CNN-Based Algorithm for Anomaly Detection in Electrocardiogram Signals (BiLSTM) JCSIS
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Evaluation of Particle Swarm Optimization for Support Vector Machine Training JCSIS
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Multitasking a deep neural network for time series load forecasting JCSIS 019928311823
info@jcsis.org JCSIS About Volumes Instructions Contact Editorials Login Register Multitasking …