A mathematical theory of sign-mediated concept formation. - Yair Neuman; Ophir Nave (Appl. Math. Comput., 2008)

Metaphor-based meaning excavation. - Yair Neuman; Ophir Nave (Inf. Sci., 2009)

Using Web-Intelligence for Excavating the Emerging Meaning of Target-Concepts. - Yair Neuman; Gabi Kedma; Yohai Cohen; Ophir Nave (Web Intelligence, 2010)

A probabilistic model of thermal explosion in polydisperse fuel spray. - Ophir Nave; Viatcheslav Bykov; Vladimir Gol'dshtein (Appl. Math. Comput., 2010)

Buzzwords on their way to a tipping-point: A view from the blogosphere. - Yair Neuman; Ophir Nave; Eran Dolev (Complex., 2011)

Analysis of the dynamics of fuel spray using asymptotic methods: The method of integral invariant manifolds. - Ophir Nave; Suraj Ajadi; Yaron Lehavi (Appl. Math. Comput., 2012)

A generic method for measuring the potential number of structure-preserving transformations. - Yair Neuman; Yohai Cohen; Zvi Bekerman; Ophir Nave (Complex., 2012)

Comparison of the Homotopy Perturbation Method (HPM) and Method of Integral Manifolds (MIM) on a Thermal Explosion of Polydisperse Fuel Spray System. - Ophir Nave; Suraj Ajadi; Yaron Lehavi; Vladimir Gol'dshtein (SIAM J. Appl. Math., 2013)

How much information should we drop to become intelligent? - Ophir Nave; Yair Neuman; Leonid I. Perlovsky; Newton Howard (Appl. Math. Comput., 2014)

Analysis of the two-dimensional polydisperse liquid sprays in a laminar boundary layer flow using the similarity transformation method. - Ophir Nave (Adv. Model. Simul. Eng. Sci., 2015)

Autodetection of a Target by Eye Movement. - Shlomo Hareli; Ophir Nave (Complex., 2016)

Analysis of a breast cancer mathematical model by a new method to find an optimal protocol for HER2-positive cancer. - Ophir Nave; Miriam Elbaz; Svetlana Bunimovich-Mendrazitsky (Biosyst., 2020)

Adding features from the mathematical model of breast cancer to predict the tumour size. - Ophir Nave (Int. J. Comput. Math. Comput. Syst. Theory, 2020)

Artificial immune system features added to breast cancer clinical data for machine learning (ML) applications. - Ophir Nave; Miriam Elbaz (Biosyst., 2021)