Results 81 to 90 of about 276,034 (269)

Predicting monthly gold prices in indian rupees using ARIMA, LSTM, GRU, and Simple Linear Regression models [PDF]

Revista Română de Informatică și Automatică
For investors and financial analysts to make informed decisions, having precise forecasts of gold prices is crucial. This study examined the effectiveness of various time series models in predicting gold prices in Indian Rupeea variety of models, ranging
Hanan ALJOHANI, Sawsan ALSHAMRANI, Nahla ALJOJO, Araek TASHKANDI, Tariq ALSAHFI   +4 more
doaj   +1 more source

AI‐based localization of the epileptogenic zone using intracranial EEG

Epilepsia Open, EarlyView.
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida, Yipeng Zhang, Sotaro Kanai, Richard Staba, Vwani Roychowdhury, Hiroki Nariai   +5 more
wiley   +1 more source

Real-time prediction of ROP based on GRU-Informer

Scientific Reports
Accurate ROP (rate of penetration) prediction contributes to better production task planning, ensuring efficient production line operation, and reducing production costs.
Bingrui Tu, Kai Bai, Ce Zhan, Wanxing Zhang   +3 more
semanticscholar   +1 more source

Tomtit‐Raven Evolutionary Selector‐Reinforced Attention‐Driven: A High‐Performance and Computationally Efficient Cyber Threat Detection Framework for Smart Grids

Energy Science &Engineering, EarlyView.
Overview of the proposed work. ABSTRACT Identifying cyber threats maintains the security and operational stability of smart grid systems because they experience escalating attacks that endanger both operating data reliability and system stability and electricity grid performance.
Priya R. Karpaga, Praveen Kumar Balachandran, Umawathy Techanamurthy, Muhammad Ammirrul Atiqi Mohd Zainuri   +3 more
wiley   +1 more source

Building an Online Learning Model Through a Dance Recognition Video Based on Deep Learning

Информатика и автоматизация
Jumping motion recognition via video is a significant contribution because it considerably impacts intelligent applications and will be widely adopted in life. This method can be used to train future dancers using innovative technology. Challenging poses
Nguyen Viet Hung, Thang Quang Loi, Nguyen Hai Binh, Nguyen Thi Thuy Nga, Truong Thu Huong, Duc Lich Luu   +5 more
doaj   +1 more source

Encoder–Decoder Based LSTM and GRU Architectures for Stocks and Cryptocurrency Prediction

Journal of Risk and Financial Management
This work addresses the intricate task of predicting the prices of diverse financial assets, including stocks, indices, and cryptocurrencies, each exhibiting distinct characteristics and behaviors under varied market conditions.
Joy Dip Das, R. Thulasiram, C. Henry, A. Thavaneswaran   +3 more
semanticscholar   +1 more source

Machine Learning and Artificial Intelligence Techniques for Intelligent Control and Forecasting in Energy Storage‐Based Power Systems

Energy Science &Engineering, EarlyView.
A new energy paradigm assisted by AI. ABSTRACT The tremendous penetration of renewable energy sources and the integration of power electronics components increase the complexity of the operation and power system control. The advancements in Artificial Intelligence and machine learning have demonstrated proficiency in processing tasks requiring ...
Balasundaram Bharaneedharan, Suresh Panchanathan, Chan Choon Kit, Elumalai Perumal Venkatesan, Nasim Hasan   +4 more
wiley   +1 more source

A Deep Neural Network Based on Two‐Stage Training for Estimating Heart Rate Variability From Camera Videos

Health Care Science, EarlyView.
Heart Rate Variability Estimation from Camera Videos ABSTRACT Background Studies have shown that heart rate variability (HRV) is a predictor of the prognosis of cardiovascular diseases. Contact heartbeat monitoring equipment is widely used, especially in hospitals, and benefits from the rapidity and accuracy of the detection of physiological health ...
Lan Lan, Jin Yin, Haohan Zhang, Hua Jiang, Rui Qin, Xia Zhao, Yu Zhang, Yilong Wang, Jiajun Qiu   +8 more
wiley   +1 more source

FloodGNN-GRU: a spatio-temporal graph neural network for flood prediction

Environmental Data Science
Classical approaches for flood prediction apply numerical methods for the solution of partial differential equations that capture the physics of inundation processes (e.g., the 2D Shallow Water equations). However, traditional inundation models are still
Arnold Kazadi, James Doss‐Gollin, Antonia Sebastian, Arlei Silva   +3 more
semanticscholar   +1 more source

Bioaccessibility of anthocyanins and bioactive compounds from Brazilian berries and their food matrix interaction: an in vitro gastrointestinal digestion study coupled to UHPLC‐ESI‐TQD‐MS/MS analysis

Journal of the Science of Food and Agriculture, EarlyView.
Abstract BACKGROUND Several commercial berries have been examined for anthocyanin composition, bioaccessibility, and bioactive effects, while Brazilian berries remain underexplored. This study aimed to access the effect of in vitro gastrointestinal digestion on anthocyanin bioaccessibility, stability, and antioxidant potential in black pitanga (Eugenia
Paulo Berni, Laís R Zandoná, Patrícia B Berilli, Livia Reguengo, Giulia Bonhin, Daniela CS Baldan, Mário R Maróstica Jr   +6 more
wiley   +1 more source