The construction process of the FCM clustering algorithm. by Xini Fang (20861990)

Subjects:

MLP vs classification algorithms. by Mohd Mustaqeem (19106494)

Construction process of RF. by Xini Fang (20861990)

Subjects:

Data_Sheet_1_Development of a Structured Query Language and Natural Language Processing Algorithm to Identify Lung Nodules in a Cancer Centre.PDF by Benjamin Hunter (2885960)

Summary of all demographic, epidemiological and clinical data presented in data set used by the primary studies. by Sebastião Rogério da Silva Neto (11944944)

Summary of all non-clinical data (laboratory and others) presented in the data set used by the primary studies. by Sebastião Rogério da Silva Neto (11944944)

Accession list and passport data. by Diego Felipe Conejo-Rodríguez (18495740)

“…<div><p>High-throughput phenotyping brings new opportunities for detailed genebank accessions characterization based on image-processing techniques and data analysis using machine learning algorithms. …”

Algorithm assessment of temporal drought class using TPM. by Muhammad Asif Khan (7367468)

Clustering evaluation results of the FCM algorithm. by Xini Fang (20861990)

Subjects:

San Diego PIRC (Primary Infection Resource Consortium) data: 1998-2019. by Sepideh Mazrouee (5191339)

The parameter settings of the RF algorithm after parameter optimization. by Xini Fang (20861990)

Subjects:

Microbial Keystone Taxa Identification in Global Wastewater Treatment Plants Based on Deep Learning by Sen Wang (135167)

“…In this work, we applied this framework to the high-throughput sequencing of the global wastewater treatment sample and identified 61 candidate taxa as the keystones for the wastewater treatment process. We found that the temperature and dissolved oxygen are the primary factors influencing the keystone taxa. …”

Microbial Keystone Taxa Identification in Global Wastewater Treatment Plants Based on Deep Learning by Sen Wang (135167)

“…In this work, we applied this framework to the high-throughput sequencing of the global wastewater treatment sample and identified 61 candidate taxa as the keystones for the wastewater treatment process. We found that the temperature and dissolved oxygen are the primary factors influencing the keystone taxa. …”

Microbial Keystone Taxa Identification in Global Wastewater Treatment Plants Based on Deep Learning by Sen Wang (135167)

“…In this work, we applied this framework to the high-throughput sequencing of the global wastewater treatment sample and identified 61 candidate taxa as the keystones for the wastewater treatment process. We found that the temperature and dissolved oxygen are the primary factors influencing the keystone taxa. …”

Microbial Keystone Taxa Identification in Global Wastewater Treatment Plants Based on Deep Learning by Sen Wang (135167)

“…In this work, we applied this framework to the high-throughput sequencing of the global wastewater treatment sample and identified 61 candidate taxa as the keystones for the wastewater treatment process. We found that the temperature and dissolved oxygen are the primary factors influencing the keystone taxa. …”

Microbial Keystone Taxa Identification in Global Wastewater Treatment Plants Based on Deep Learning by Sen Wang (135167)

“…In this work, we applied this framework to the high-throughput sequencing of the global wastewater treatment sample and identified 61 candidate taxa as the keystones for the wastewater treatment process. We found that the temperature and dissolved oxygen are the primary factors influencing the keystone taxa. …”

Illustration of the texton detection process. by Vivian Akoto-Adjepong (18165075)

Phenomic and classical descriptors. by Diego Felipe Conejo-Rodríguez (18495740)

“…<div><p>High-throughput phenotyping brings new opportunities for detailed genebank accessions characterization based on image-processing techniques and data analysis using machine learning algorithms. …”

Overview of the algorithmic approach and its place in the hypothesis generation toolkit. by Braden T. Tierney (8822813)

Features selected by optimization algorithms. by Afnan M. Alhassan (18349378)

“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”