[HTML][HTML] Application of artificial intelligence in food industry—a guideline

NR Mavani, JM Ali, S Othman, MA Hussain… - Food Engineering …, 2022 - Springer
Artificial intelligence (AI) has embodied the recent technology in the food industry over the
past few decades due to the rising of food demands in line with the increasing of the world …

[HTML][HTML] Covid-19 outbreak prediction with machine learning

SF Ardabili, A Mosavi, P Ghamisi, F Ferdinand… - Algorithms, 2020 - mdpi.com
Several outbreak prediction models for COVID-19 are being used by officials around the
world to make informed decisions and enforce relevant control measures. Among the …

Systematic review of deep learning and machine learning models in biofuels research

S Ardabili, A Mosavi, AR Várkonyi-Kóczy - International Conference on …, 2019 - Springer
The importance of energy systems and their role in economics and politics is not hidden for
anyone. This issue is not only important for the advanced industrialized countries, which are …

[HTML][HTML] COVID-19 pandemic prediction for Hungary; a hybrid machine learning approach

G Pinter, I Felde, A Mosavi, P Ghamisi, R Gloaguen - Mathematics, 2020 - mdpi.com
Several epidemiological models are being used around the world to project the number of
infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate …

Comparative analysis of ANN-ICA and ANN-GWO for crop yield prediction

S Nosratabadi, K Szell, B Beszedes… - … on Computing and …, 2020 - ieeexplore.ieee.org
Prediction of crops yield is essential for food security policymaking, planning, and trade. The
objective of the current study is to propose novel crop yield prediction models based on …

Hybrid machine learning model of extreme learning machine radial basis function for breast cancer detection and diagnosis; a multilayer fuzzy expert system

S Mojrian, G Pinter, JH Joloudari, I Felde… - … on Computing and …, 2020 - ieeexplore.ieee.org
Mammography is often used as the most common laboratory method for the detection of
breast cancer, yet associated with the high cost and many side effects. Machine learning …

Predicting Antecedents of Employee Smart Work Adoption Using SEM‐Multilayer Perceptron Approach

WB Wang, CJ Shieh, HMR Al-Khafaji… - Human Behavior …, 2023 - Wiley Online Library
The COVID‐19 pandemic forced many organizations to move to telework and smart work
(SW), and this practice is expected to continue even later in the postpandemic period …

Artificial Intelligence models and employee lifecycle management: A systematic literature review

S Nosratabadi, RK Zahed, VV Ponkratov, EV Kostyrin - Organizacija, 2022 - sciendo.com
Background and purpose: The use of artificial intelligence (AI) models for data-driven
decision-making in different stages of employee lifecycle (EL) management is increasing …

Identification of impurity in wheat mass based on video processing using artificial neural network and PSO algorithm

S AgaAzizi, M Rasekh… - Journal of Food …, 2021 - Wiley Online Library
The presence of weed seeds and other impurities in the wheat grains affect the identification
quality of the wheat grains. This study explores the possibility of identifying the wheat in the …

Hybrid machine learning models for crop yield prediction

S Nosratabadi, F Imre, K Szell, S Ardabili… - arXiv preprint arXiv …, 2020 - arxiv.org
Prediction of crop yield is essential for food security policymaking, planning, and trade. The
objective of the current study is to propose novel crop yield prediction models based on …