Neural networks in fisheries research
暂无分享,去创建一个
G. N. Rao | Gollapalli Nageswara Rao | Iragavarapu Suryanarayana | Antonio Braibanti | Rupenaguntla Sambasiva Rao | Veluri Anantha Ramam | Duvvuri Sudarsan | A. Braibanti | I. Suryanarayana | D. Sudarsan | V. Ramam | R. Rao | G. N. Rao
[1] Young-Seuk Park,et al. Stream fish assemblages and basin land cover in a river network. , 2006, Science of the Total Environment.
[2] N. Ramani,et al. Fish detection and identification using neural networks-some laboratory results , 1992 .
[3] Anthony J. Richardson,et al. Relating sardine recruitment in the Northern Benguela to satellite-derived sea surface height using a neural network pattern recognition approach , 2003 .
[4] Inmaculada Pulido-Calvo,et al. Comparison between traditional methods and artificial neural networks for ammonia concentration forecasting in an eel (Anguilla anguilla L.) intensive rearing system , 2004 .
[5] D. M. Titterington,et al. Statistics and Neural Networks , 2000, Technometrics.
[6] C. Koenig,et al. Elemental composition of otoliths used to trace estuarine habitats of juvenile gag Mycteroperca microlepis along the west coast of Florida , 2004 .
[7] D. Krishna,et al. Chemometric investigation of complex equilibria in solution phase II: Sensitivity of chemical models for the interaction of AADH and FAH with Ni(II) in aqueous medium. , 1993, Talanta.
[8] Donald A. Jackson,et al. Fish–Habitat Relationships in Lakes: Gaining Predictive and Explanatory Insight by Using Artificial Neural Networks , 2001 .
[9] Einar Eg Nielsen,et al. Assigning individual fish to populations using microsatellite DNA markers , 2001 .
[10] Junjun Chang,et al. Microsatellites assessment of Chinese sturgeon (Acipenser sinensis Gray) genetic variability , 2005 .
[11] Vasilis D. Valavanis,et al. Time series analysis and forecasting techniques applied on loliginid and ommastrephid landings in Greek waters , 2006 .
[12] Nitin Muttil,et al. Genetic programming for analysis and real-time prediction of coastal algal blooms , 2005 .
[13] Ulf Dieckmann,et al. Age at maturation predicted from routine scale measurements in Norwegian spring-spawning herring (Clupea harengus) using discriminant and neural network analyses , 2003 .
[14] T. Brey,et al. Artificial neural networks to forecast biomass of Pacific sardine and its environment , 1996 .
[15] Jae Ho Sohn,et al. Process studies of odour emissions from effluent ponds using machine-based odour measurement , 2006 .
[16] J. Giske,et al. Ecology in Mare Pentium: an individual-based spatio-temporal model for fish with adapted behaviour , 1998 .
[17] H. M. Mohammed. Population dynamics and exploitation of Metapenaeus affinis in Kuwaiti waters , 1995 .
[18] Yong-Woo Lee,et al. Comparative analysis of statistical tools to identify recruitment–environment relationships and forecast recruitment strength , 2005 .
[19] Edward W. Large,et al. Auditory Temporal Computation: Interval Selectivity Based on Post-Inhibitory Rebound , 2002, Journal of Computational Neuroscience.
[20] Can Ozan Tan,et al. Modeling complex nonlinear responses of shallow lakes to fish and hydrology using artificial neural networks , 2006 .
[21] Hal S. Stern,et al. Neural networks in applied statistics , 1996 .
[22] J. Figuerola,et al. Co-occurrence patterns of some small-bodied freshwater fishes in southwestern France: implications for fish conservation and environmental management. , 2005 .
[23] Alexander K. Morison,et al. A trial of artificial neural networks for automatically estimating the age of fish , 1999 .
[24] James H. Thorne,et al. PREDICTING OCCURRENCES AND IMPACTS OF SMALLMOUTH BASS INTRODUCTIONS IN NORTH TEMPERATE LAKES , 2004 .
[25] Barbara Rasco,et al. Nondestructive Determination of Moisture and Sodium Chloride in Cured Atlantic Salmon (Salmo salar) (Teijin) Using Short‐wavelength Near‐infrared Spectroscopy (SW‐NIR) , 2003 .
[26] K R Svoboda,et al. Interactions between the neural networks for escape and swimming in goldfish , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[27] Tae-Soo Chon,et al. Using an artificial neural network to patternize long-term fisheries data from South Korea , 2005, Aquatic Sciences.
[28] J. Jaworska,et al. Influence of ecological factors on the relationship between mfo induction and fish growth: Bridging the gap using neural networks , 1996 .
[29] G. Swartzman,et al. Bayesian estimation of fish school cluster composition applied to a Bering Sea acoustic survey , 2001 .
[30] S. Lek,et al. Spatial organisation of European eel (Anguilla anguilla L.) in a small catchment , 2003 .
[31] J. Raga,et al. Parasite infracommunities as predictors of harvest location of bogue (Boops boops L.): a pilot study using statistical classifiers , 2005 .
[32] G. Huse. Modelling habitat choice in fish using adapted random walk , 2001 .
[33] D. M. Titterington,et al. Neural Networks: A Review from a Statistical Perspective , 1994 .
[34] Shijie Zhou. Application of Artificial Neural Networks for Forecasting Salmon Escapement , 2003 .
[35] R. Death,et al. Predictive modelling and spatial mapping of freshwater fish and decapod assemblages using GIS and neural networks , 2004 .
[36] Marc Mangel,et al. Explicit trade-off rules in proximate adaptive agents , 2003 .
[37] Cynthia M. Jones,et al. Accurate classification of juvenile weakfish Cynoscion regalis to estuarine nursery areas based on chemical signatures in otoliths , 1998 .
[38] K. Wieland,et al. Prediction of vertical distribution and ambient development temperature of Baltic cod, Gadus morhua L., eggs , 1997 .
[39] A Atkinson,et al. Using fragment chemistry data mining and probabilistic neural networks in screening chemicals for acute toxicity to the fathead minnow , 2004, SAR and QSAR in environmental research.
[40] Anthony J. Richardson,et al. Identification and classification of vertical chlorophyll patterns in the Benguela upwelling system and Angola-Benguela front using an artificial neural network , 2001 .
[41] J. Crivello,et al. THE CONTRIBUTION OF EGG-BEARING FEMALE AMERICAN LOBSTER (HOMARUS AMERICANUS) POPULATIONS TO LOBSTER LARVAE COLLECTED IN LONG ISLAND SOUND BY COMPARISON OF MICROSATELLITE ALLELE FREQUENCIES , 2009 .
[42] W. Pitts,et al. A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.
[43] J. Huuskonen. QSAR modeling with the electrotopological state indices: predicting the toxicity of organic chemicals. , 2003, Chemosphere.
[44] Sovan Lek,et al. Modelling of microhabitat used by fish in natural and regulated flows in the river Garonne (France) , 2001 .
[45] A. Brierley,et al. Identification of Southern Ocean acoustic targets using aggregation backscatter and shape characteristics , 2003 .
[46] Teruhisa Komatsu,et al. Prediction of the Catch of Japanese Sardine Larvae in Sagami Bay Using a Neural Network , 1994 .
[47] Duc Truong Pham,et al. Neural Networks for Identification, Prediction and Control , 1995 .
[48] Geir Huse,et al. Artificial Evolution of Life History and Behavior , 2002, The American Naturalist.
[49] Georg H. Engelhard,et al. Maturity changes in Norwegian spring-spawning herring before, during, and after a major population collapse , 2004 .
[50] B. A. Johnson,et al. A Device to Measure Shell Hardness of Dungeness Crabs and Trial Application in the Kodiak Island, Alaska, Commercial Fishery , 1999 .
[51] Michel Dreyfus-León,et al. A spatial individual behaviour-based model approach of the yellowfin tuna fishery in the eastern Pacific Ocean , 2001 .
[52] J. M. Kim,et al. Effects of sudden changes in salinity on endogenous rhythms of the spotted sea bass Lateolabrax sp. , 1998 .
[53] Michel Dreyfus-León,et al. Analysis of non-linear relationships between catch per unit effort and abundance in a tuna purse-seine fishery simulated with artificial neural networks , 2004 .
[54] S. Lek,et al. The use of artificial neural networks to predict the presence of small‐bodied fish in a river , 1997 .
[55] Yoshiki Kashimori,et al. A neural model of electrosensory system making electrolocation of weakly electric fish , 2001, Neurocomputing.
[56] Steven R. Hare,et al. Neural network and fuzzy logic models for pacific halibut recruitment analysis , 2006 .
[57] P. Laffaille,et al. Habitat preferences of different European eel size classes in a reclaimed marsh: A contribution to species and ecosystem conservation , 2004, Wetlands.
[58] Sovan Lek,et al. Artificial neural networks as a tool in ecological modelling, an introduction , 1999 .
[59] Thomas Brey,et al. Exploring the use of neural networks for biomass forecasts in the Peruvian upwelling ecosystem , 1995 .
[60] Paul T. Gayes,et al. Spatially quantitative seafloor habitat mapping: example from the northern South Carolina inner continental shelf , 2004 .
[61] G. Engelhard,et al. Maturation characteristics in Norwegian spring-spawning herring before, during, and after a major population collapse , 2002 .
[62] Costas Papaconstantinou,et al. Predicting demersal fish species distributions in the Mediterranean Sea using artificial neural networks , 2003 .
[63] Sovan Lek,et al. Energy availability and habitat heterogeneity predict global riverine fish diversity , 1998, Nature.
[64] Marit Aursand,et al. Bioactive compounds in cod (Gadus morhua) products and suitability of 1H NMR metabolite profiling for classification of the products using multivariate data analyses. , 2005, Journal of agricultural and food chemistry.
[65] Kayhan Gulez,et al. Design of a robust neural network structure for determining initial stability particulars of fishing vessels , 2004 .
[66] James E. McKenna,et al. Application of Neural Networks to Prediction of Fish Diversity and Salmonid Production in the Lake Ontario Basin , 2005 .
[67] E. Rochard,et al. Discrimination of the natal origin of young‐of‐the‐year Allis shad (Alosa alosa) in the Garonne–Dordogne basin (south‐west France) using otolith chemistry , 2005 .
[68] Cornelius T. Leondes,et al. Neural network systems techniques and applications , 1998 .
[69] Sovan Lek,et al. Predicting local fish species richness in the garonne river basin , 1998 .
[70] Sovan Lek,et al. Abundance, diversity, and structure of freshwater invertebrates and fish communities: An artificial neural network approach , 2001 .
[71] Sovan Lek,et al. Utilisation of non-supervised neural networks and principal component analysis to study fish assemblages , 2001 .
[72] Ronan Fablet,et al. Automated fish age estimation from otolith images using statistical learning , 2005 .
[73] Sovan Lek,et al. Stochastic models that predict trout population density or biomass on a mesohabitat scale , 1996, Hydrobiologia.
[74] Mohd Azlan Hussain,et al. Prediction of pores formation (porosity) in foods during drying: generic models by the use of hybrid neural network , 2002 .
[75] J. M. Hammond,et al. A semiconducting metal-oxide array for monitoring fish freshness , 2002 .
[76] I. Dimopoulos,et al. Application of neural networks to modelling nonlinear relationships in ecology , 1996 .
[77] J P Grubert,et al. Acid deposition in the eastern United States and neural network predictions for the future , 2003 .
[78] T M Martin,et al. Prediction of the acute toxicity (96-h LC50) of organic compounds to the fathead minnow (Pimephales promelas) using a group contribution method. , 2001, Chemical research in toxicology.
[79] T. Neelakantan,et al. Artificial Neural Network Prediction of Viruses in Shellfish , 2005, Applied and Environmental Microbiology.
[80] S. Mastrorillo,et al. Using self-organizing maps to investigate spatial patterns of non-native species , 2005 .
[81] Bernardino Arcay Varela,et al. Optimisation of fishing predictions by means of artificial neural networks, anfis, functional networks and remote sensing images , 2005, Expert Syst. Appl..
[82] Irene Gregory-Eaves,et al. Tailoring palaeolimnological diatom-based transfer functions , 2004 .
[83] Jeffrey A. Falke,et al. Modelling of stream fishes in the Great Plains, USA , 2005 .
[84] J. Olden. A Species‐Specific Approach to Modeling Biological Communities and Its Potential for Conservation , 2003 .
[85] Tae-Soo Chon,et al. Pattern recognition of the movement tracks of medaka (Oryzias latipes) in response to sub-lethal treatments of an insecticide by using artificial neural networks. , 2002, Environmental pollution.
[86] Lou Wen. Sea water quality assessment model using artificial neural networks , 2001 .
[87] Lou Wen-gao. Eutrophication assessment model using artificial neural networks for lakes and reservoirs , 2001 .
[88] S. Lek,et al. Studying the spatiotemporal variation of the littoral fish community in a large prealpine lake, using self-organizing mapping , 2005 .
[89] M. Ryan,et al. Vestigial preference functions in neural networks and túngara frogs. , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[90] J. Simmonds,et al. Species identification using wideband backscatter with neural network and discriminant analysis , 1996 .
[91] Sovan Lek,et al. Predicting the abundance of minnow Phoxinus phoxinus (Cyprinidae) in the River Ariège (France) using artificial neural networks , 1997 .
[92] I. Aoki,et al. Prediction of the Path Type and Offshore Distance of the Kuroshio Current Using Neural Network , 1994 .
[93] Werner Brack,et al. MODELKEY. Models for assessing and forecasting the impact of environmental key pollutants on freshwater and marine ecosystems and biodiversity (5 pp) , 2005, Environmental science and pollution research international.
[94] Masashi Sugiyama,et al. Incremental projection learning for optimal generalization , 2001, Neural Networks.
[95] Sovan Lek,et al. Relationships between Environmental Characteristics and the Density of Age‐0 Eurasian Perch Perca fluviatilis in the Littoral Zone of a Lake: A Nonlinear Approach , 2002 .
[96] Nobuo Kimura,et al. Forecasting the Rolling Motion of Small Fishing Vessels for Scallop-Hanging Culture under Fishing Operation , 2004 .