Results 91 to 100 of about 60,295 (249)

Image segmentation using fuzzy LVQ clustering networks [PDF]

In this note we formulate image segmentation as a clustering problem. Feature vectors extracted from a raw image are clustered into subregions, thereby segmenting the image.
Bezdek, James C., Pal, Nikhil R., Tsao, Eric Chen-Kuo   +2 more
core   +1 more source

Learning Vector Quantization with Training Count (LVQTC)

Neural Networks, 1997
Kohonen's learning vector quantization (LVQ) is modified by attributing training counters to each neuron, which record its training statistics. During training, this allows for dynamic self-allocation of the neurons to classes. In the classification stage training counters provide an estimate of the reliability of classification of the single neurons ...
openaire   +3 more sources

Integrating Artificial Intelligence With Droplet‐Based Microfluidics: Advances, Challenges, and Emerging Opportunities

Advanced Intelligent Systems, EarlyView.
Droplet‐based microfluidics enables precise, high‐throughput microscale reactions but continues to face challenges in scalability, reproducibility, and data complexity. This review examines how artificial intelligence enhances droplet generation, detection, sorting, and adaptive control and discusses emerging opportunities for clinical and industrial ...
Junyan Lai, Sukrit Gaira, Rongfeng Wang, Qingtian Zhang, Ming Li, Liao Wu, Diangeng Li, Yi Li, Chengchen Zhang, Jinhong Guo, Shi‐Yang Tang   +10 more
wiley   +1 more source

Medical diagnosis using artificial neural networks

Mathematics in Applied Sciences and Engineering
Medical diagnosis using Artificial Neural Networks (ANN) and computer-aided diagnosis with deep learning is currently a very active research area in medical science.
Afsana Begum, Md. Masiur Rahman, Sohana Jahan   +2 more
doaj   +1 more source

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source

Penerapan Learning Vector Quantization (LVQ) untuk Klasifikasi Status Gizi Anak

IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2013
Abstrak Penentuan klasifikasi status gizi anak yang sering dilakukan adalah berdasarkan indeks berat badan menurut tinggi badan (BB/TB). Pada Puskesmas Batupanjang, indeks antropometri tersebut dihitung secara manual untuk menilai status gizi anak ...
Elvia Budianita, Widodo Prijodiprodjo
doaj   +1 more source

Electroencephalography (EEG) eye state classification using learning vector quantization and bagged trees. [PDF]

Heliyon, 2023
Nilashi M, Abumalloh RA, Ahmadi H, Samad S, Alghamdi A, Alrizq M, Alyami S, Nayer FK.   +7 more
europepmc   +1 more source

Matrix Learning in Learning Vector Quantization

, 2006
We propose a new matrix learning scheme to extend Generalized Relevance Learning Vector Quantization (GRLVQ), an efficient prototype-based classification algorithm. By introducing a full matrix of relevance factors in the distance measure, correlations between different features and their importance for the classification scheme can be taken into ...
Biehl, M., Hammer, Barbara, Schneider, P.   +2 more
openaire   +4 more sources

A Flexible and Energy‐Efficient Compute‐in‐Memory Accelerator for Kolmogorov–Arnold Networks

Advanced Intelligent Systems, EarlyView.
This article presents KA‐CIM, a compute‐in‐memory accelerator for Kolmogorov–Arnold Networks (KANs). It enables flexible and efficient computation of arbitrary nonlinear functions through cross‐layer co‐optimization from algorithm to device. KA‐CIM surpasses CPU, ASIC, VMM‐CIM, and prior KAN accelerators by 1–3 orders of magnitude in energy‐delay ...
Chirag Sudarshan, Oliver Artner, Paul‐Philipp Manea, Sebastian Siegel, Susanne Hoffmann‐Eifert, Regina Dittmann, John Paul Strachan   +6 more
wiley   +1 more source

Methods for Setting Device Specifications for Analog In‐Memory Computing Inference

Advanced Intelligent Systems, EarlyView.
Non‐volatile memories (NVMs) are being developed for analog in‐memory computing for energy‐efficient, high‐speed deep learning inference. As technology is moving to industry adoption, a method to define required NVM specifications is critical for improving performance and reducing manufacturing cost.
Zhenyu Wu, Xin Su, Malte J. Rasch, Ning Li   +3 more
wiley   +1 more source