arXiv subjects

Saeed Setayeshi

Publications and source records attributed to Saeed Setayeshi.

4 recordsLinked to original sources

arXiv2025

Design an Ontology for Cognitive Business Strategy Based on Customer Satisfaction

Ontology is a general term used by researchers who want to share information in a specific domain. One of the hallmarks of the greatest success of a powerful manager of an organization is his ability to interpret unplanned and unrelated events. Tools to solve this problem are vital to business growth. Modern technology allows customers to be more informed and influential in their roles as patrons and critics. This can make or break a business. Research shows that businesses that employ a customer-first strategy and prioritize their customers can generate more revenue. Even though there are many different Ontologies offered to businesses, none of it is built from a cognitive perspective. The objective of this study is to address the concept of strategic business plans with a cognitive ontology approach as a basis for a new management tool. This research proposes to design a cognitive ontology model that links customer measurement with traditional business models, define relationships between components and verify the accuracy of the added financial value.

cs.CY↗

arXiv2024

Solving the decision-making differential equations from eye fixation data in Unity software by using Hermite Long-Short-Term Memory neural network

Cognitive decision-making processes are crucial aspects of human behavior, influencing various personal and professional domains. This research delves into the application of differential equations in analyzing decision-making accuracy by leveraging eye-tracking data within a virtual industrial town setting. The study unveils a systematic approach to transforming raw data into a differential equation, essential for deciphering the relationship between eye movements during decision-making processes. Mathematical relationship extraction and variable-parameter definition pave the way for deriving a differential equation that encapsulates the growth of fixations on characters. The key factors in this equation encompass the fixation rate $(\lambda)$ and separation rate $(\mu)$, reflecting user interaction dynamics and their impact on decision-making complexities tied to user engagement with virtual characters. For a comprehensive grasp of decision dynamics, solving this differential equation requires initial fixation counts, fixation rate, and separation rate. The formulation of differential equations incorporates various considerations such as engagement duration, character-player distance, relative speed, and character attributes, enabling the representation of fixation changes, speed dynamics, distance variations, and the effects of character attributes. This comprehensive analysis not only enhances our comprehension of decision-making processes but also provides a foundational framework for predictive modeling and data-driven insights for future research and applications in cognitive science and virtual reality environments.

cs.HC↗

arXiv2012

An Iterative Procedure for Optimal Control of Bilinear Systems

This paper presents a new and straightforward procedure for solving bilinear quadratic optimal control problem. In this method, first the original optimal control problem is transformed into a nonlinear twopoint boundary value problem (TPBVP) via the Pontryagin's maximum principle. Then, the nonlinear TPBVP is transformed into a sequence of linear time-invariant TPBVPs using the homotopy perturbation method (HPM) and introducing a convex homotopy in topologic space. Solving the latter problems through an iterative process yields the optimal control law and optimal trajectory in the form of infinite series. Finally, sufficient condition for convergence of these series is proved by a theorem. Simplicity and efficiency of the proposed method is shown through an illustrative example.

math.OC↗

arXiv2012

Comparing Methods for segmentation of Microcalcification Clusters in Digitized Mammograms

The appearance of microcalcifications in mammograms is one of the early signs of breast cancer. So, early detection of microcalcification clusters (MCCs) in mammograms can be helpful for cancer diagnosis and better treatment of breast cancer. In this paper a computer method has been proposed to support radiologists in detection MCCs in digital mammography. First, in order to facilitate and improve the detection step, mammogram images have been enhanced with wavelet transformation and morphology operation. Then for segmentation of suspicious MCCs, two methods have been investigated. The considered methods are: adaptive threshold and watershed segmentation. Finally, the detected MCCs areas in different algorithms will be compared to find out which segmentation method is more appropriate for extracting MCCs in mammograms.

cs.CV↗