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61

Hamiltonian DNN-Optimized Adaptive Droop Control for Balancing Battery State of Charge in DC Microgrids by the RBMO.
Edwin Deepak, F. X. ; Srinivasan, A. ; Rajani, B. ; et al.
High Energy Chemistry; Dec2025, Vol. 59 Issue 6, p565-585, 21p

DEEP learning BATTERY management syste... RESEARCH methodology eva... VOLTAGE control MICROGRIDS OPTIMIZERS (Computer sof... …
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62

FUTURE MARKET TRENDS PREDICTION WITH PYTHON AND MACHINE LEARNING.
Dalawat, Lakshyaraj Singh ; Soni, Divya ; Jain, Lovisha ; et al.
International Journal of Advanced Research in Computer Science; 2022 Special Issue, Vol. 13, p92-96, 5p

FUTURES market ECONOMIC trends TECHNOLOGICAL forecastin... STOCK prices STOCK exchanges PYTHON programming langu... …
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63

Advanced analysis of temporal data using Fisher-Shannon information: theoretical development and application in geosciences
Guignard, Fabian ; Laib, Mohamed ; Amato, Federico ; et al.
Front. Earth Sci. 8:255 (2020)

Statistics - Methodology
Report
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64

Unveiling the Spatial Distribution of Heat Demand in North-West-Europe Compiled with National Heat Consumption Data.
Jüstel, Alexander ; Humm, Elias ; Herbst, Eileen ; et al.
Energies (19961073); Jan2024, Vol. 17 Issue 2, p481, 36p

BRUSSELS (Belgium) EUROPEAN Union GREENHOUSE gases GEOGRAPHIC information s... DEMAND forecasting GEOTHERMAL resources …
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65

Advancement of an (in vitro/ex vivo) hybrid model framework to forecast polyviral lung disease outcomes.
Varalakshmi S ; Vijayalakshmi P ; Rajendran V
Publisher: SAGE Publications Country of Publication: England NLM ID: 9501229 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1708-8267 (Electronic) Linking ISSN: 10815589 NLM ISO Abbreviation: J Investig Med Subsets: MEDLINE

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66

КАТАЛИТКАЛЫҚ КРЕКИНГ ҚОНДЫРҒЫСЫНАН ӨНІМНІҢ ШЫҒУЫН PYTHON БАҒДАРЛАМАЛЫҚ ОРТАСЫН ҚОЛДАНУ АРҚЫЛЫ АНЫҚТАУ. (Kazakh)
Боранбаева, Н. ; Оразбаев, Б. ; Рзаева, Л. ; et al.
Vestnik KazUTB; 2024, Vol. 4 Issue 25, p84-96, 13p

CATALYTIC cracking PYTHON programming langu... PETROLEUM refining MANUFACTURING processes PETROLEUM refineries
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67

The application of deep learning in economic analysis and marketing strategy formulation in the tourism industry.
Zhang, Jing ; Gao, Ming
PLoS ONE; 6/6/2025, Vol. 20 Issue 6, p1-23, 23p

ECONOMIC forecasting INTERNATIONAL tourism TOURISM DEEP learning ECONOMIC impact
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68

Prediction of electrical load demand using combined LHS with ANFIS.
Ismail, Ahmed G. ; Elbanna, Sayed H. A. ; Mohamed, Hassan S.
PLoS ONE; 6/10/2025, Vol. 20 Issue 6, p1-29, 29p

MACHINE learning LATIN hypercube sampling ELECTRICAL load ENERGY consumption POWER resources DEMAND forecasting
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69

Prediction of rubber vulcanization using an artificial neural network.
Lubura, Jelena D. ; Kojić, Predrag ; Pavličević, Jelena ; et al.
Chemical Industry / Hemijska Industrija. 2021, Vol. 75 Issue 5, p277-283. 7p.

Artificial neural networ... Rubber Vulcanization Sequential learning Machine learning Forecasting …
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70

Optimization of the FRi3D CME Model in EUHFORIA.
Flossie, M. ; Maharana, A. ; Linan, L. ; et al.
Space Weather: The International Journal of Research & Applications; Sep2025, Vol. 23 Issue 9, p1-21, 21p

CORONAL mass ejections SOLAR wind DATA assimilation SPACE environment MATHEMATICAL programming
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71

Forecasting ETF Performance: A Comparative Study of Deep Learning Models and the Fama-French Three-Factor Model.
Shih, Kuang-Hsun ; Wang, Yi-Hsien ; Kao, I-Chen ; et al.
Mathematics (2227-7390); Oct2024, Vol. 12 Issue 19, p3158, 15p

ARTIFICIAL neural networ... BUSINESS forecasting DECISION making in inves... INVESTMENT policy ARTIFICIAL intelligence DEEP learning
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72

Progressive time series analysis: CNN-LSTM models for stock market trend prediction.
Pandeeswari, G. Janani alias
AIP Conference Proceedings; 2025, Vol. 3204 Issue 1, p1-10, 10p

STOCK price forecasting MACHINE learning INVESTORS STOCKS (Finance) STOCK prices PRAGMATICS
Konferenz
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73

Process mining analysis for value added services
Universitat Politècnica de Catalunya. Departament de Ciències de la Computació ; HP Printing and Computing ; Escolano Peinado, Carlos ; et al.

E-Ressource
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74

The effect of lossy compression of numerical weather prediction data on data analysis: a case study using enstools-compression 2023.11.
Tintó Prims, Oriol ; Redl, Robert ; Rautenhaus, Marc ; et al.
Geoscientific Model Development; 2024, Vol. 17 Issue 24, p8909-8925, 17p

NUMERICAL weather foreca... ATMOSPHERIC sciences EARTH scientists DATA integrity EARTH sciences
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75

Optimizing Sentiment Analysis Models for Customer Support: Methodology and Case Study in the Portuguese Retail Sector.
Almeida, Catarina ; Castro, Cecilia ; Leiva, Víctor ; et al.
Journal of Theoretical & Applied Electronic Commerce Research. Jun2024, Vol. 19 Issue 2, p1493-1516. 24p.

Natural language process... Sentiment analysis Portuguese language Consumers Retail industry
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76

Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0).
Pulkkinen, Seppo ; Nerini, Daniele ; Pérez Hortal, Andrés A. ; et al.
Geoscientific Model Development; 2019, Vol. 12 Issue 10, p4185-4219, 35p

FINLAND METEOROLOGICAL precipita... PYTHON programming langu... OPTICAL flow SEVERE storms HYDROLOGISTS …
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77

AIRWAVE-SLSTR—An Algorithm to Estimate the Total Column of Water Vapour from SLSTR Measurements over Liquid Surfaces.
Castelli, Elisa ; Casadio, Stefano ; Papandrea, Enzo ; et al.
Remote Sensing; Apr2025, Vol. 17 Issue 7, p1205, 22p

LAND surface temperature NUMERICAL weather foreca... OCEAN temperature WATER vapor LIQUID surfaces
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79

A Study on Selected Stock Price of Fast-Moving Consumer Goods Sector
Nitish Kulkarni ; Ramya H P ; Nitish Kulkarni ; et al.
Journal of Accounting Research, Business and Finance Management (e-ISSN: 2582-8851); Volume 5, Issue 2 (July- December) 2024; 32-39; 2582-8851

E-Ressource
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80

Enhancing resilience of distributed DC microgrids against cyber attacks using a transformer-based Kalman filter estimator.
Rostami, Seyyed Mohammad Hosseini ; Pourgholi, Mahdi ; Asharioun, Hadi
Scientific Reports; 2/25/2025, Vol. 15 Issue 1, p1-29, 29p

LONG short-term memory BOX-Jenkins forecasting ARTIFICIAL intelligence CYBERTERRORISM TRANSFORMER models DEEP learning
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