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9th International Conference on Smart City Applications, SCA 2024

作者： Taidi, Loubna El Khamlichi, Sokaina Maurady, Amal Laboratory of Innovative Technology Faculty of Sciences and Technologies Abdelmalek Essaadi University Tangier Morocco LyRICA–Laboratory of Research in Informatics Data Sciences and Artificial Intelligence School of Information Sciences Rabat Morocco

ISBN: (纸本)9783031886522

COVID-19 pandemic has created a serious risk for public health around the world. Due to the absence of an effective therapy against coronavirus 2, the number of infected patients and deaths has been very high. In silico tools have been useful for researching possible medications, reusing existing established therapies, and doing bioinformatics investigation of SARS-CoV-2 structures. The structure-based virtual screening (SBVS) was applied on the highly comparable medication remdesivir, chosen from the PubChem database to supply novel, very stable major protease (Mpro) inhibitors for SARS-CoV-2. The analysis was performed on 32 compounds, utilizing the AutoDock VINA tool associated with PyRx 0.8. Considering the energy of ligand binding, four compounds were chosen and then were subject to prediction of pharmacokinetic properties. Among the 30 selected compounds, CID51041118 was selected as high-potent anti- COVID-19. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： COVID-19

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Comparative analysis of Elman and Jordan Recurrent Neural Networks for Solar Radiation and Air Temperature Prediction Using Backpropagation Variants

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第18期49卷 87-98页

作者： Ed-Daoudi, Rachid Azhari, Mourad Ettaki, Badia Zerouaoui, Jamal LyRICA: Laboratory of Research in Informatics Data Sciences and Artificial Intelligence School of Information Sciences B.P. 604 Rabat-Instituts Rabat Morocco Center of Guidance and Educational Planning B.P. 6222 Rabat-Instituts Rabat Morocco Laboratory of Engineering Sciences and Modeling Faculty of Sciences Ibn Tofail University Campus Universitaire KenitraBP 133 Morocco

This study compares the effectiveness of Elman Recurrent Neural Networks (ERNN) and Jordan Recurrent Neural Networks (JRNN) for predicting solar radiation and ambient temperature in Ouarzazate, Morocco. Data collected at 10-minute intervals from three meteorological stations (OUA_001, OUA_002, and OUA_003) over a 3–4-year period (2018-2022) were analyzed. The dataset was split into training (75%) and validation (25%) subsets to develop and test the models. The research systematically explored different network architectures with 1-2 hidden layers containing 4-12 neurons, applying three learning algorithms: BackPropagation (BP), BackPropagation with Momentum (BPM), and Resilient Backpropagation (Rprop). Key hyperparameters were optimized within specific ranges, including learning rates (0.00001-0.4) for BP/BPM and weight decay exponents (0.00001-4) for Rprop. Input variables included date, temperature, solar radiation, wind speed, relative humidity, precipitation, and atmospheric pressure in various combinations. Performance evaluation using Nash-Sutcliffe Efficiency (NSE) and Index of Agreement (d) revealed high prediction accuracy for both model types, with values exceeding 0.9 during validation. JRNN with BPM performed best at station OUA_001 (NSE: 0.909 for radiation, 0.971 for temperature), while ERNN with BPM demonstrated superior performance at station OUA_002 (NSE: 0.978 for radiation, 0.976 for temperature). At station OUA_003, both models showed comparable results when using BP. Despite the high overall accuracy, both models exhibited limitations in predicting extreme solar radiation values, particularly during nighttime hours. The study concludes that ERNN and JRNN are effective tools for short-term prediction of solar radiation and temperature in arid regions like Ouarzazate, though further refinement is needed to better capture extreme values and improve prediction accuracy during transitional periods. © 2025 Slovene Society Informatika. All rights reser

关键词： backpropagation prediction recurrent neural networks solar radiation temperature

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