Results 71 to 80 of about 42,084 (260)

Review on Predictive Modelling Techniques for Identifying Students at Risk in University Environment

MATEC Web of Conferences, 2019
Predictive analytics including statistical techniques, predictive modelling, machine learning, and data mining that analyse current and historical facts to make predictions about future or otherwise unknown events.
Nik Nurul Hafzan Mat Yaacob, Safaai Deris, Asiah Mat, Mohd Saberi Mohamad, Siti Syuhaida Safaai   +4 more
doaj   +1 more source

A Dynamic Bayesian Network Approach for Analysing Topic-Sentiment Evolution

IEEE Access, 2020
Sentiment analysis is one of the key tasks of natural language understanding. Sentiment Evolution models the dynamics of sentiment orientation over time.
Huizhi Liang, Umarani Ganeshbabu, Thomas Thorne   +2 more
doaj   +1 more source

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

dbnR: Gaussian Dynamic Bayesian Network Learning and Inference in R

Journal of Statistical Software
Dynamic Bayesian networks are a type of multivariate time series forecasting model capable of a level of interpretability thanks to their graphical representation.
David Quesada, Pedro Larrañaga, Concha Bielza   +2 more
doaj   +1 more source

Multistream Dynamic Bayesian Network for Meeting Segmentation [PDF]

, 2005
This paper investigates the automatic analysis and segmentation of meetings. A meeting is analysed in terms of individual behaviours and group interactions, in order to decompose each meeting in a sequence of relevant phases, named meeting actions. Three feature families are extracted from multimodal recordings: prosody from individual lapel microphone
Alfred Dielmann, Steve Renals
openaire   +3 more sources

Advancing Lithium–Oxygen Batteries: Pioneering Cathode Catalyst Innovation and Artificial Intelligence‐Driven Design Paradigms

Advanced Materials, EarlyView.
This review summarizes the principles and challenges of nonaqueous lithium‐oxygen batteries and recent advances in cathode catalysts, including carbon‐based materials, metals, oxides, sulfides, nitrides, carbides, and redox mediators. It highlights emerging design strategies and artificial intelligence‐driven approaches, emphasizing data‐assisted ...
Yuqing Yao, Chongyang Hao, Karol Viviana Mejia‐Centeno, Malik Dilshad Khan, Shang Wang, Longqiu Li, Yanhong Tian, Andreu Cabot, Qing Sun   +8 more
wiley   +1 more source

Sensorimotor coupling via Dynamic Bayesian Networks

2008 IEEE International Conference on Robotics and Automation, 2008
In this paper we consider the problem of sensorimotor coordination in a Bayesian framework. To this end we introduce a novel kind of Dynamic Bayesian Network serving as the core tool to integrate active vision and task-constrained motor behaviors. The proposed system is put into work by addressing the challenging task of realistic drawing performed by ...
Ruben Coen Cagli, Paolo Napoletano, Paolo Coraggio, Giuseppe Boccignone, Agostino De Santis   +4 more
openaire   +3 more sources

Weaving Intelligence: Thermally Drawn Multimaterial Fibers Toward AI‐Enabled Smart Textiles

Advanced Materials, EarlyView.
Thermally drawn multimaterial fibers are rapidly advancing as intelligent structural units for next‐generation smart textiles. Integrating multimaterial architectures with neuromorphic and spiking‐neural‐network principles enables fabrics that can sense, compute, and adapt autonomously.
Vuong Dinh Trung, Jiaxiang Yi, Hieu Nguyen‐Duc, Tien Nu Hoang Lo, Weili Zhao, Chaoqun Dong, Fabien Sorin, Baris Caglar, Lei Wei, Tung Nguyen‐Dang   +9 more
wiley   +1 more source

Bidirectional Process Prediction in the Laser‐Induced‐Graphene Production Using Blackbox Deep Learning

Advanced Materials Technologies, EarlyView.
This study shows that a lightweight blackbox neural network provides a practical, cost‐effective solution for bidirectional process prediction in laser‐induced graphene (LIG) fabrication. Achieving high predictive performance with minimal overhead, the approach democratizes machine learning (ML) for resource‐limited environments.
Maxim Polomoshnov, Nitheesh M. Nair, Gerardo Hernández‐Sosa, Markus Reischl   +3 more
wiley   +1 more source

Data‐Efficient Electromagnetic Surrogate Solver Through Dissipative Relaxation Transfer Learning

Advanced Optical Materials, EarlyView.
Dissipative relaxation transfer learning (DIRTL) enables data‐efficient training of electromagnetic surrogate solvers by pretraining data generated with artificial material loss before fine‐tuning on target lossless data. The framework suppresses resonant outlier effects during early training, allowing effective adaptation to high‐amplitude resonances ...
Sunghyun Nam, Chan Y. Park, Min Seok Jang   +2 more
wiley   +1 more source