Results 71 to 80 of about 16,939 (236)

Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network

, 2018
A deep learning network was used to predict future blood glucose levels, as this can permit diabetes patients to take action before imminent hyperglycaemia and hypoglycaemia.
Mougiakakou, Stavroula Georgia, Marko V. Jankovic, Sun, Qingnan, Stavroula G. Mougiakakou, Bally, Lia, Qingnan Sun, Jankovic, Marko, Lia Bally   +7 more
core   +1 more source

Modelling customers credit card behaviour using bidirectional LSTM neural networks

, 2021
With the rapid growth of consumer credit and the huge amount of financial data developing effective credit scoring models is very crucial. Researchers have developed complex credit scoring models using statistical and artificial intelligence (AI ...
Abbod, Maysam F., Majdalawieh, Munir, Ala’raj, Maher   +2 more
core   +2 more sources

Automating AI Discovery for Biomedicine Through Knowledge Graphs and Large Language Models Agents

Advanced Intelligent Discovery, EarlyView.
This work proposes a novel framework that automates biomedical discovery by integrating knowledge graphs with multiagent large language models. A biologically aligned graph exploration strategy identifies hidden pathways between biomedical entities, and specialized agents use this pathway to iteratively design AI predictors and wet‐lab validation ...
Naafey Aamer, Muhammad Nabeel Asim, Shan Munir, Andreas Dengel   +3 more
wiley   +1 more source

A Multimodal Intelligent System for Human Digital Twin Simulation with Continuous Kinematic Data Tracking, Biometric Prognosis, and Cognitive State Feedback in Industrial Environments

Advanced Intelligent Discovery, EarlyView.
This article implements a unified human digital twin framework that integrates cutting edge actuation, sensing, simulation, and bidirectional feedback capability. The approach includes integrating multimodal sensing, AI, and biomechanical simulation into one compact system.
Tajbeed Ahmed Chowdhury, Sebastian Gratz‐Kelly, Eric Wagner, Paul Motzki, Martina Lehser   +4 more
wiley   +1 more source

Planetary gearbox fault diagnosis using bidirectional-convolutional LSTM networks

, 2022
Gearbox fault diagnosis is expected to significantly improve the reliability, safety and efficiency of power transmission systems. However, planetary gearbox fault diagnosis remains a challenge due to complex responses caused by multiple planetary gears.
Shi, Junchuan,, Peng, Zhongxiao,, Goebel, Kai,, Wu, Dazhong,, Zhang, Ziyang,, Peng, Dikang,   +5 more
core   +1 more source

Large Language Model‐Based Chatbots in Higher Education

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The use of large language models (LLMs) in higher education can facilitate personalized learning experiences, advance asynchronized learning, and support instructors, students, and researchers across diverse fields. The development of regulations and guidelines that address ethical and legal issues is essential to ensure safe and responsible adaptation
Defne Yigci, Merve Eryilmaz, Ail K. Yetisen, Savas Tasoglu, Aydogan Ozcan   +4 more
wiley   +1 more source

A High‐Precision Dynamic Movement Recognition Algorithm Using Multimodal Biological Signals for Human–Machine Interaction

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This article describes a multimodal fusion data acquisition and processing system about electromyography for dynamic movement recognition and bioelectrical impedance for key posture recognition. In addition, a new dynamic–static fusion algorithm strategy is designed.
Chenhao Cao, Gang Ma, Zelin Chen, Yiming Ouyang, Hu Jin, Shiwu Zhang   +5 more
wiley   +1 more source

An Enhanced LSTM Approach for Detecting IoT-Based DDoS Attacks Using Honeypot Data

International Journal of Computational Intelligence Systems
One of the widening perils in network security is the Distributed Denial of Service (DDoS) attacks on the Internet of Things (IoT) ecosystem. This paper presents an enhanced Intrusion Detection System (IDS) through the proposal of an enhanced version of ...
Arjun Kumar Bose Arnob, M. F. Mridha, Mejdl Safran, Md Amiruzzaman, Md. Rajibul Islam   +4 more
doaj   +1 more source

Identifying Dynamical Quantum Phase Transitions With a Migratable Quantum‐Classical Hybrid Neural Network

Advanced Intelligent Systems, EarlyView.
A hybrid quantum‐classical architecture is introduced to accurately identify dynamical quantum phase transitions from time‐evolved quantum states. The QCNN serves as a quantum dynamical feature extractor, while the classical network learns temporal correlations from a low‐dimensional readout sequence. The framework attains high accuracy, remains robust
Daili Li, Xin–Xin Yang, Xiaoting Wang, Wei Zhang   +3 more
wiley   +1 more source

Bidirectional LSTM Recurrent Neural Network for Keyphrase Extraction [PDF]

, 2017
To achieve state-of-the-art performance, keyphrase extraction systems rely on domain-specific knowledge and sophisticated features. In this paper, we propose a neural network architecture based on a Bidirectional Long Short-Term Memory Recurrent Neural Network that is able to detect the main topics on the input documents without the need of defining ...
Marco Basaldella, ANTOLLI, ELISA, Giuseppe Serra, Carlo Tasso   +3 more
openaire   +2 more sources