Saied Tadayon’s scientific contributions

Here, we introduce Z-webs, including Z-factors and Z-nodes, for the understanding of relationships between objects, subjects, abstract ideas, concepts, or the like, including face, car, images, people, emotions, mood, text, natural language, voice, music, video, locations, formulas, facts, historical data, landmarks, personalities, ownership, family, friends, love, happiness, social behavior, voting behavior, and the like, to be used for many applications in our life, including on the search engine, analytics, Big Data processing, natural language processing, economy forecasting, face recognition, dealing with reliability and certainty, medical diagnosis, pattern recognition, object recognition, biometrics, security analysis, risk analysis, fraud detection, satellite image analysis, machine generated data analysis, machine learning, training samples, extracting data or patterns (from the video, images, and the like), editing video or images, and the like. Z-factors include reliability factor, confidence factor, expertise factor, bias factor, and the like, which is associated with each Z-node in the Z-web.

In this chapter, we present a method for approximate evaluation of Zadeh’s Z-numbers using category sets of probability distributions corresponding to similar certainty measures.

We present an iterative preprocessing approach for training a support vector machine for a large dataset, based on balancing the center of mass of input data within a variable margin about the hyperplane. At each iteration, the input data is projected on the hyperplane, and the imbalance of the center of mass for different classes within a variable margin is used to update the direction of the hyperplane within the feature space. The approach provides an estimate for the margin and the regularization constant. In the case of fuzzy membership of the data, the membership function of the input data is used to determine center of mass and to count data points which violate the margin. An extension of this approach to non-linear SVM is suggested based on dimension estimation of the feature space represented via a set of orthonormal feature vectors.

Novel methods of IP or patent management and monetization based on IP/patent pooling and scoring systems are disclosed. In addition, novel partnership methods with IP producing entities (such as universities) are disclosed which produce incentives and efficiencies far and above other methods. Systems and methods are disclosed for valuation of IP instruments and distribution of IP revenue/proceeds. Examples of methods for scoring IP instruments, using a transactional and event driven point/value system, are disclosed for tracking, monitoring, distribution and allocation of proceeds in a complex pooling arrangement of IP instruments.