Machine learning in perovskite solar cells: recent developments and future perspectives

NK Bansal, S Mishra, H Dixit, S Porwal… - Energy …, 2023 - Wiley Online Library
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 …

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 …

UncertaintyFuseNet: robust uncertainty-aware hierarchical feature fusion model with ensemble Monte Carlo dropout for COVID-19 detection

M Abdar, S Salari, S Qahremani, HK Lam, F Karray… - Information …, 2023 - Elsevier
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 …

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 …

Optimal design of convolutional neural network architectures using teaching–learning-based optimization for image classification

KM Ang, ESM El-Kenawy, AA Abdelhamid, A Ibrahim… - Symmetry, 2022 - mdpi.com
Convolutional neural networks (CNNs) have exhibited significant performance gains over
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

Y Wang, Q Li, X Guan, J Fan, M Xu, H Wang - Knowledge-Based Systems, 2021 - Elsevier
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 …

[HTML][HTML] An experimental analysis of different deep learning based models for Alzheimer's disease classification using brain magnetic resonance images

RA Hazarika, D Kandar, AK Maji - … of King Saud University-Computer and …, 2022 - Elsevier
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 …

Convolutional neural network pruning based on multi-objective feature map selection for image classification

P Jiang, Y Xue, F Neri - Applied soft computing, 2023 - Elsevier
Deep convolutional neural networks (CNNs) are widely used for image classification. Deep
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

Y Zeiträg, JR Figueira, N Horta, R Neves - Expert Systems with Applications, 2022 - Elsevier
Dispatching rules are simple but efficient heuristics to solve multi-objective job 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

X Wang, Y Wang, L Tang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
High quality product quality prediction is very important for iron and steel enterprises to
ensure stable production. However, most existing prediction methods are manually …