Results 91 to 100 of about 30,985 (264)

Structured RNN for human interaction

IET Computer Vision, 2018
Understanding human activities has been an important research area in computer vision. Generally, the authors can model the human interactions as a temporal sequence with the transition in relationships of humans and objects. Besides, many studies have proved the effectiveness of long short‐term memory (LSTM) on long‐term temporal dependency problems ...
Anh Minh Truong, Atsuo Yoshitaka
openaire   +2 more sources

Exploiting Ferroelectric and Spintronic Dynamics for Neural Network Computation

Advanced Intelligent Systems, EarlyView.
Ferroelectric and spintronic devices, relying on the control of polarization and magnetization, offer intrinsically fast, durable, energy‐efficient, and low‐latency building blocks for analog in‐memory computing. The hysteretic dynamics of an order parameter are leveraged to provide nonvolatile, multistate memory and nonlinear switching. Brain‐inspired
Dashiell Harrison, Kevin Nadaud, Josquin Harrison, Guillaume F. Nataf, Laura Bégon‐Lours   +4 more
wiley   +1 more source

Application of Deep Learning Algorithm for Web Shell Detection in Web Application Security System

Jurnal Sisfokom
A web shell is a script executed on a web server, often used by hackers to gain control over an infected server. Detecting web shells is challenging due to their complex behavior patterns. This research focuses on using a deep learning approach to detect
Rezky Yuranda, Edi Surya Negara
doaj   +1 more source

Real‐Time Biomass Estimation in High‐Density Yeast Fermentations Using Soft Sensor Modeling

Biotechnology and Bioengineering, EarlyView.
Accurate biomass measurements, both offline and online, are essential to improve prediction and control in yeast fermentation. This study develops regression‐based predictive models to correlate offline measurements (Dry Cell Weight and OD600 from a spectrophotometer) with online OD860 probe signals to provide accurate real‐time biomass estimations and
Ana G. Del Hierro, José Luis Checa‐Barrera, Juan A. Moreno‐Cid, Eoin Casey   +3 more
wiley   +1 more source

Digital Technology's Role in Circular Waste Management: A Systematic Review

Business Strategy and the Environment, EarlyView.
ABSTRACT Combining circular economy ideas with digital tools offers a game‐changing way to tackle global sustainability problems. This paper focuses on how digital changes and circular economy models link up. A review has been conducted for 112 articles from 2021 to September 2025, using PRISMA‐2020 methodology. This study covered new tech like AI, IoT,
Reza Eslamipoor
wiley   +1 more source

Artificial Intelligence Tools for Carbon Nanotube Research: Opportunities From Synthesis to Applications

Carbon and Hydrogen, EarlyView.
Artificial intelligence tools are reshaping carbon nanotube research by connecting synthesis, characterization, and application‐oriented design. This review outlines how supervised learning, deep learning, Bayesian optimization, and large language models accelerate data extraction, experiment planning, and structure–property discovery for carbon ...
Yanlong Zhao, Fei Wang, Qixuan Cai, Aike Xi, Kangkang Wang, Khaixien Leu, Rufan Zhang   +6 more
wiley   +1 more source

Perovskite Photodetectors at the Intelligence Frontier: From Tunable Materials to Adaptive Optoelectronic Systems

Carbon Energy, EarlyView.
This comprehensive review presents a progressive roadmap for perovskite vision detectors. Centered on perovskite‐based artificial perception, the graphic illustrates a systematic evolution: starting with fundamental material engineering and device architectures, advancing toward complex functional strategies such as flexible neuromorphic imaging ...
Chenglong Li, Wei Qu, Deepak Kumar, Naini Jain, Nur Wardina Syahirah binti Mohamad Fadil, Nur Najiha binti Ahmad Rasid, Emannuel Kymakis, Maria Vasilopoulou, Chunxiong Bao, Feng Gao, Peng Gao, Sanjeev Kumar Sharma, Mohammad Khaja Nazeeruddin, Abd. Rashid bin Mohd Yusoff, Haotong Wei   +14 more
wiley   +1 more source

A Graph‐Based Generative Artificial Intelligence Methodology for Autocorrection of Utility‐System P&IDs

Chemie Ingenieur Technik, EarlyView.
This work explores generative AI for automated revision of Piping and Instrumentation Diagrams (P&IDs). We frame P&ID correction as a translation problem, converting attributed P&ID graphs into sequences and learning revisions with a transformer‐based model.
Lukas Schulze Balhorn, Dominik P. Goldstein, Niels Seijsener, Kevin Dao, Ge H. M. Driessen, Artur M. Schweidtmann   +5 more
wiley   +1 more source

Transformers are Multi-State RNNs

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Transformers are considered conceptually different from the previous generation of state-of-the-art NLP models - recurrent neural networks (RNNs). In this work, we demonstrate that decoder-only transformers can in fact be conceptualized as unbounded multi-state RNNs - an RNN variant with unlimited hidden state size.
Matanel Oren, Michael Hassid, Yarden Nir, Yossi Adi, Roy Schwartz 0001   +4 more
openaire   +2 more sources

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