Results 141 to 150 of about 11,198,889 (292)

Improved Fuzzy Possibilistic C-Means using Artificial Bee Colony for Clustering New Student’s Financial Capability to Determine Tuition Level

JOIV: International Journal on Informatics Visualization
Outliers in the dataset will affect the quality of the cluster, so a good clustering method is needed. Based on the Mahalanobis distance method, it is known that the research dataset has outliers.
Edi Satriyanto, Ni Wayan Surya Wardhani, Syaiful Anam, Wayan Firdaus Mahmudy   +3 more
doaj   +1 more source

Microbiome−host proteostasis crosstalk—An emerging perspective on mechanisms and interventions toward healthy longevity

FEBS Letters, EarlyView.
Proteostasis and the gut microbiota play a key role in shaping host physiology. Microbiota‐derived metabolites, vitamins, and RNA modulate host proteostasis. Findings from model systems, including C. elegans, indicate microbes can either stabilize or disrupt host proteostasis.
Abhishek Anil Dubey, Maria Ermolaeva
wiley   +1 more source

Parametric Comparison of K-means and Adaptive K-means Clustering Performance on Different Images

, 2017
Image segmentation takes a major role to analyzing the area of interest in image processing. Many researchers have used different types of techniques to analyzing the image. One of the widely used techniques is K-means clustering.
Parvathi, K., Sahu, Madhusmita, K. Parvathi, Madhusmita Sahu, M. Vamsi Krishna, Krishna, M. Vamsi   +5 more
core   +2 more sources

Upcoming Conferences

Bulletin of the History of Archaeology, 2009
Bernard K. Means
doaj   +1 more source

From mice to humans—divergent strategies for intestinal homeostasis and regeneration

FEBS Letters, EarlyView.
Recent advances such as organoid genome editing, xenotransplantation, imaging, and whole‐genome sequencing have enabled direct studies of human intestinal stem cells (ISCs). These studies reveal species‐specific features, including slower ISC proliferation, distinct injury responses, slower somatic mutation accumulation in humans, and an inverse ...
Keiko Ishikawa, Shinya Sugimoto, Toshiro Sato   +2 more
wiley   +1 more source

Analisis Karakteristik Mahasiswa Berdasarkan Nilai Kelompok Mata Kuliah dengan Menggunakan Analisis Cluster K-Means

, 2019
Indeks Prestasi (IP) adalah nilai yang dihitung berdasarkan jumlah beban studi yang diambil dalam satu semester dikalikan dengan bobot prestasi tiap-tiap mata kuliah kemudian dibagi dengan jumlah beban kredit yang diambil. IPK merupakan IP kumulatif dari
Yahdin, Sugandi, Sania, Rana, Irmeilyana, Irmeilyana   +2 more
core   +1 more source

FT K-Means: A High-Performance K-Means on GPU with Fault Tolerance

2024 IEEE International Conference on Cluster Computing (CLUSTER)
K-means is a widely used algorithm in clustering, however, its efficiency is primarily constrained by the computational cost of distance computing. Existing implementations suffer from suboptimal utilization of computational units and lack resilience against soft errors.
Shixun Wu, Yitong Ding, Yujia Zhai, Jinyang Liu 0003, Jiajun Huang 0001, Zizhe Jian, Huangliang Dai, Sheng Di, Bryan M. Wong, Zizhong Chen, Franck Cappello   +10 more
openaire   +2 more sources

PARK(ing) time–How park deficiency affects the biological clock in a Drosophila model of Parkinson's disease

FEBS Letters, EarlyView.
Drosophila park mutants serve as a model for Parkinson's disease. We used this strain to investigate the connection between oxidative stress and the circadian clock mechanism. We showed that increased oxidative stress affects the physiology of pacemaker cells, disrupting their daily structural plasticity. Lack of rhythmic signaling from pacemaker cells
Kamila Zientara, Kinga Skoczek, Elzbieta Pyza, Milena Damulewicz   +3 more
wiley   +1 more source

Analysis on local optimum existence form of K-means-type

, 2014
With the hypothesis of Gaussian distribution of patterns, K-means and its extensions are good for clustering. As the representative of partitional clustering algorithm, K-means follows rules for running: numbers of clusters to be set, cluster ...
Xia QH(夏庆华), Zhao, Fei, Zhang, CN, Xia Qinhua, Zhao Fei, Zou Yuanyuan, Zhang CN(张承宁), Zou YY(邹媛媛), Zhang Chengning   +8 more
core  

Global k-means++: an effective relaxation of the global k-means clustering algorithm

Applied Intelligence
The $k$-means algorithm is a prevalent clustering method due to its simplicity, effectiveness, and speed. However, its main disadvantage is its high sensitivity to the initial positions of the cluster centers. The global $k$-means is a deterministic algorithm proposed to tackle the random initialization problem of k-means but its well-known that ...
Georgios Vardakas, Aristidis Likas
openaire   +2 more sources