Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters

Species Richness ◽

Aquatic Insect ◽

Insect Species ◽

Running Waters ◽

Artificial Neural ◽

Insect Species Richness

A comparison of artificial neural networks and random forests to predict native fish species richness in Mediterranean rivers

Knowledge and Management of Aquatic Ecosystems ◽

10.1051/kmae/2013052 ◽

2013 ◽

pp. 07 ◽

Cited By ~ 22

Author(s):

E.J. Olaya-Marín ◽

F. Martínez-Capel ◽

P. Vezza

Keyword(s):

Neural Networks ◽

Species Richness ◽

Random Forests ◽

Fish Species ◽

Native Fish ◽

Mediterranean Rivers ◽

Modeling the species richness and abundance of lotic macroalgae based on habitat characteristics by artificial neural networks: a potentially useful tool for stream biomonitoring programs

Journal of Applied Phycology ◽

10.1007/s10811-017-1107-5 ◽

2017 ◽

Vol 29 (4) ◽

pp. 2145-2153 ◽

Cited By ~ 3

Author(s):

José Celso Rocha ◽

Cleto K. Peres ◽

José Leonel L. Buzzo ◽

Vinícius de Souza ◽

Eric A. Krause ◽

...

Keyword(s):

Neural Networks ◽

Species Richness ◽

Habitat Characteristics ◽

Artificial Neural ◽

Species Richness And Abundance

Sensitivity of aquatic insect species richness to disturbance in the Adour–Garonne stream system (France)

Ecological Indicators ◽

10.1016/s1470-160x(03)00016-5 ◽

2003 ◽

Vol 3 (2) ◽

pp. 135-142 ◽

Cited By ~ 37

Author(s):

Arthur Compin ◽

Régis Céréghino

Keyword(s):

Species Richness ◽

Aquatic Insect ◽

Insect Species ◽

Stream System ◽

Insect Species Richness

Patterning and predicting aquatic insect richness in four West-African coastal rivers using artificial neural networks

Knowledge and Management of Aquatic Ecosystems ◽

10.1051/kmae/2010029 ◽

2010 ◽

pp. 06 ◽

Cited By ~ 2

Author(s):

E.O. Edia ◽

M. Gevrey ◽

A. Ouattara ◽

S. Brosse ◽

G. Gourène ◽

...

Keyword(s):

Neural Networks ◽

Aquatic Insect ◽

West African ◽

Coastal Rivers ◽

Determination of S-N curves with the application of artificial neural networks

Fatigue & Fracture of Engineering Materials & Structures ◽

10.1046/j.1460-2695.1999.00198.x ◽

1999 ◽

Vol 22 (8) ◽

pp. 723-728 ◽

Cited By ~ 1

Author(s):

Artymiak ◽

Bukowski ◽

Feliks ◽

Narberhaus ◽

Zenner

Keyword(s):

Neural Networks ◽

Applications of artificial neural networks to adaptive training systems.

PsycEXTRA Dataset ◽

10.1037/e456072004-001 ◽

1992 ◽

Author(s):

Peter L. Rothman

Keyword(s):

Neural Networks ◽

Adaptive Training ◽

Training Systems ◽

Use of Artificial Neural Networks for Risk Assessment for Work and Organizational Issues

PsycEXTRA Dataset ◽

10.1037/e666722007-001 ◽

2006 ◽

Author(s):

Maria Karanika ◽

Tom Cox

Keyword(s):

Neural Networks ◽

Risk Assessment ◽

Organizational Issues ◽

Artificial neural networks for diagnosis and prognosis in prostate cancer

Seminars in Urologic Oncology ◽

10.1053/suro.2002.32492 ◽

2002 ◽

Vol 20 (2) ◽

pp. 89-95 ◽

Cited By ~ 35

Author(s):

Guido Schwarzer ◽

Martin Schumacher

Keyword(s):

Prostate Cancer ◽

Neural Networks ◽

Diagnosis And Prognosis ◽

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

Scientific Review Series 1 Economics and Law ◽

10.33051/2076-4650-2018-6-6-11 ◽

2018 ◽

pp. 6-11

Author(s):

Kobiljon Kh. Zoidov ◽

◽

Svetlana V. Ponomareva ◽

Daniel I. Serebryansky ◽

◽

...

Keyword(s):

Neural Networks ◽

Space Exploration ◽

Differentiation Between Mass-forming Type Peripheral Cholangiocarcinoma and Hepatic Abscesses: Application of Artificial Neural Networks to CT Images

Journal of the Korean Radiological Society ◽

10.3348/jkrs.2005.53.5.343 ◽

2005 ◽

Vol 53 (5) ◽

pp. 343 ◽

Cited By ~ 1

Author(s):

Nak Jong Seong ◽

Jeong Min Lee ◽

Se Hyung Kim ◽

Joon Koo Han ◽

Young Jun Kim ◽

...

Keyword(s):

Neural Networks ◽

Ct Images ◽

Peripheral Cholangiocarcinoma ◽