Results 101 to 110 of about 102,701 (252)

PERFORMANCE EVALUATION OF SEASONAL ARIMA-SVR AND SEASONAL ARIMAX-SVR HYBRID METHODS ON FORECASTING PADDY PRODUCTION

Barekeng
This study explores advances in forecasting time series data by combining linear and non-linear models. Traditional methods such as ARIMA and its variant ARIMAX are effective for linear data but have limitations when dealing with non-linearity.
I'lmisukma Risnawati, Farit Mochamad Afendi, I Made Sumertajaya   +2 more
doaj   +1 more source

Data‐driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models

The Canadian Journal of Chemical Engineering, EarlyView.
Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro   +3 more
wiley   +1 more source

PREDICTION OF UNIT VALUE INDEX OF EXPORTS OF SITC 897 JEWELRY AND PRECIOUS GOODS GROUP IN INDONESIA

Barekeng
Export is an international trade activity that plays an important role in the economic progress in Indonesia. One of Indonesia's leading commodities that dominate the export market is jewelry.
Grace Lucyana Koesnadi, Bagas Shata Pratama, Dzuria Hilma Qurotu Ain, Elly Pusporani, M. Fariz Fadillah Mardianto   +4 more
doaj   +1 more source

Hybrid machine learning and genetic algorithm approach for catalyst and process optimization in Fischer–Tropsch synthesis toward sustainable fuel production

The Canadian Journal of Chemical Engineering, EarlyView.
Graphical representation of a data‐driven framework for Fischer‐Tropsch synthesis (FTS) modelling and optimization. Abstract This study presents a data‐driven approach for predicting the relationships between catalyst design, process conditions, and product selectivity in Fischer–Tropsch synthesis (FTS).
Doaa M. Hassan, Ryan Gosselin, Nicolas Abatzoglou   +2 more
wiley   +1 more source

Dynamic geo‐hydrogeological monitoring‐driven situational awareness for real‐time floor water inrush risk prediction in deep mining

Deep Underground Science and Engineering, EarlyView.
The fused data extracted from the distributed monitoring system as the data basis, combined with dynamic geological data, are imported into a deep learning model. As the geological conditions of mining and excavation change, the risk of water inrush at the working face is retrieved in real time.
Yongjie Li, Huiyong Yin, Fangying Dong, Tao Wu, Chao Zhang   +4 more
wiley   +1 more source

Verteilungsfragen in Deutschland: Herausforderungen der Messung und der zielgerichteten Umverteilung

Wirtschaftsdienst, 2020
Lars P. Feld, Jan L. Fries, Malte Preuß, Christoph M. Schmidt   +3 more
doaj   +1 more source

Comparative Study of Linear Regression, SVR, and XGBoost for Stock Price Prediction After a Stock Split

Journal of Applied Informatics and Computing
This study aims to identify the most effective regression method for predicting the closing stock price of Bank Central Asia (BBCA) following the stock split event on October 12, 2021.
Muhammad Yusuf Andrika, Majid Rahardi
doaj   +1 more source

AI‐Driven Precision Annealing for High Performance Fe‐Based Amorphous Alloys

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
The four stages of the research process are as follows: First, data is collected and a database is constructed. This is followed by feature selection and analysis, then the establishment of machine learning models, and finally formulation design and preparation.
Yichuan Tang, Shaopeng Liu, Silong Li, Ruonan Ma, Peinan Li, Zheng Wang, Kun Wang, Kaiyan Cao, Sidan Ding, Chao Zhou, Fanghua Tian, Sen Yang, Minxia Fang, Yin Zhang   +13 more
wiley   +1 more source

Pharmaförderung ausschließlich aus Steuermitteln

Monitor Versorgungsforschung
„Preise innovativer Arzneimittel in einem lernenden Gesundheitssystem“, lautet der Titel des aktuellen Gutachtens 2025 des Sachverständigenrats zur Begutachtung der Entwicklung im Gesundheitswesen und in der Pflege (SVR) – ein Thema übrigens, das der Rat
Peter Stegmaier
doaj   +1 more source

AI‐driven circular economy optimization in waste management: A review of current evidence

Environmental Progress &Sustainable Energy, EarlyView.
Abstract The integration of artificial intelligence (AI) and machine learning (ML) in waste management has the potential to significantly advance circular economy objectives by enhancing efficiency, reducing waste, and optimizing resource recovery. However, realising these benefits depends on addressing significant technical, economic, and systemic ...
David Bamidele Olawade, Ram Narayan Yadav, James O Ijiwade, Ojima Z. Wada   +3 more
wiley   +1 more source