Machine learning in perovskite solar cells: recent developments and future perspectives
Within a short period of time, perovskite solar cells (PSC) have attracted paramount research
interests among the photovoltaic (PV) community. Usage of machine learning (ML) into PSC …
interests among the photovoltaic (PV) community. Usage of machine learning (ML) into PSC …
A novel combined approach based on deep Autoencoder and deep classifiers for credit card fraud detection
H Fanai, H Abbasimehr - Expert Systems with Applications, 2023 - Elsevier
Due to the growth of e-commerce and online payment methods, the number of fraudulent
transactions has increased. Financial institutions with online payment systems must utilize …
transactions has increased. Financial institutions with online payment systems must utilize …
UncertaintyFuseNet: robust uncertainty-aware hierarchical feature fusion model with ensemble Monte Carlo dropout for COVID-19 detection
Abstract The COVID-19 (Coronavirus disease 2019) pandemic has become a major global
threat to human health and well-being. Thus, the development of computer-aided detection …
threat to human health and well-being. Thus, the development of computer-aided detection …
Heuristic and metaheuristic methods for the parallel unrelated machines scheduling problem: a survey
M Ɖurasević, D Jakobović - Artificial Intelligence Review, 2023 - Springer
Scheduling has an immense effect on various areas of human lives, be it though its
application in manufacturing and production industry, transportation, workforce allocation, or …
application in manufacturing and production industry, transportation, workforce allocation, or …
Optimal design of convolutional neural network architectures using teaching–learning-based optimization for image classification
Convolutional neural networks (CNNs) have exhibited significant performance gains over
conventional machine learning techniques in solving various real-life problems in …
conventional machine learning techniques in solving various real-life problems in …
Collaborative multi-depot pickup and delivery vehicle routing problem with split loads and time windows
Optimization of collaborative multi-depot pickup and delivery logistics networks (CMDPDLN)
with split loads and time windows involves a customer demand splitting strategy and a multi …
with split loads and time windows involves a customer demand splitting strategy and a multi …
[HTML][HTML] An experimental analysis of different deep learning based models for Alzheimer's disease classification using brain magnetic resonance images
Classification of Alzheimer's disease (AD) is one of the most challenging issues for
neurologists. Manual methods are time consuming and may not be accurate all the time …
neurologists. Manual methods are time consuming and may not be accurate all the time …
Convolutional neural network pruning based on multi-objective feature map selection for image classification
Deep convolutional neural networks (CNNs) are widely used for image classification. Deep
CNNs often require a large memory and abundant computation resources, limiting their …
CNNs often require a large memory and abundant computation resources, limiting their …
[HTML][HTML] Surrogate-assisted automatic evolving of dispatching rules for multi-objective dynamic job shop scheduling using genetic programming
Dispatching rules are simple but efficient heuristics to solve multi-objective job shop
scheduling problems, particularly useful to face the challenges of dynamic shop …
scheduling problems, particularly useful to face the challenges of dynamic shop …
Multi-objective ensemble learning with multi-scale data for product quality prediction in iron and steel industry
High quality product quality prediction is very important for iron and steel enterprises to
ensure stable production. However, most existing prediction methods are manually …
ensure stable production. However, most existing prediction methods are manually …