Results 251 to 260 of about 4,317,504 (301)

Leaftronics: Bio‐Fractal Scaffolds From Leaf Venation for Low‐Waste Electronics

Advanced Materials, EarlyView.
“Leaftronics” transforms naturally evolved leaf venation into quasi‐fractal scaffolds for sustainable electronics. Polymer‐infiltrated leaf skeletons can be used to fabricate ultra‐smooth, reflow‐ and thin‐film‐compatible decomposable substrates, while making the same lignocellulose networks conducting results in flexible transparent electrodes.
Rakesh Rajendran Nair, Hrisheekesh Thachoth Chandran, Karl Leo, Hans Kleemann   +3 more
wiley   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Multi-Label Classification

International Journal of Data Warehousing and Mining, 2007
Multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization, and semantic scene classification. This article introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative ...
Grigorios Tsoumakas, Ioannis Katakis 0001   +1 more
core   +4 more sources

Multi label classification of discrete data

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2013
The paper describes an algorithm for multi-label classification. Since a pattern can belong to more than one class, the task of classifying a test pattern is a challenging one. We propose a new algorithm to carry out multi-label classification which works for discrete data.We have implemented the algorithm and presented the results for different multi ...
Bhupesh Akhand, V. Susheela Devi
openaire   +3 more sources

Multi-label Dysfluency Classification

, 2022
Stuttering is a neuro-developmental disorder represented in 1% of the population. Dysfluency classification is still an open research question, with concerns of which feature representation or which classifier to use.
Mélanie Jouaiti, Kerstin Dautenhahn
openaire   +3 more sources

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Multi-label classification of feedbacks

Journal of Intelligent & Fuzzy Systems, 2021
This work deals with educational text mining, a field of natural language processing applied to education. The objective is to classify the feedback generated by teachers in online courses to the activities sent by students according to the model of Hattie and Timperley (2007), considering that feedback may be at the levels task, process, regulation ...
Dorian Ruiz Alonso, Claudia Zepeda Cortés, Hilda Castillo Zacatelco, José Luis Carballido Carranza, José Luis García-Cué   +4 more
openaire   +1 more source

A Study on Multi-label Classification

2013
Multi-label classifications exist in many real world applications. This paper empirically studies the performance of a variety of multi-label classification algorithms. Some of them are developed based on problem transformation. Some of them are developed based on adaption.
Clifford A. Tawiah, Victor S. Sheng
openaire   +2 more sources