Purpose The purpose of this study is to examine the proper structure for the integrated reporting of hi-tech knowledge-based organizations (KBOs); in particular, the authors evaluate the appropriateness of the concept and elements of integrated reporting for hi-tech KBOs. Design/methodology/approach The study uses an exploratory sequential mixed-method approach, including an initial qualitative case study, then an instrument development phase (Delphi), and finally, a quantitative survey. Findings The resulting analysis concluded that hi-tech KBOs have the potential to prepare a simplified, integrated report. The organization overview, governance, business model, strategies and resource allocation, performance, opportunities and risks are the content elements relevant to be included in the hi-tech KBOs annual report. However, the organization’s future outlook is not confirmed to be included. Due to liability and competitive concerns, organizations do not provide targets, forecasts, projections or even scenarios. Research limitations/implications Focus on a single country and a small sample of interviewees participated. Practical implications This paper concludes that the existing integrated reporting framework is useful for different types of organizations, but with some modifications. In addition, it analyzes how directors of Iranian hi-tech KBOs perceive and value content elements of integrated reporting. Social implications This paper suggests that the fulfillment of corporate transparency for Iranian hi-tech KBOs can be achieved by the policymaker’s support on integrated reporting. Originality/value Iran is swiftly moving toward a knowledge-based economy, and hi-tech KBOs will become the powerhouse of the economy. It is important to understand how managers of Iranian hi-tech KBOs perceive and value integrated reporting. The previous practical studies are not focused on Iranian firms and the impacts of integrated reporting on hi-tech KBOs and its implementation and effectiveness had not been studied before.

A novel method for classification of objects based on a hybrid of decision theoretic and structural methods is presented in this paper. The circumstances of scaling and presence of noise are included as the part of the study. Images are degraded by some known types of noise like Gaussian, Salt & Pepper and Speckle and iterative filtering algorithm based on classification results and using alpha-trimmed mean filter will be used. Fuzzy clustering algorithm is used for thresholding and background removal in cluttered images and spurious parts are reduced using morphological operations. Input database is made up of images having similar shapes lied on their most usual appearance. Feature vectors are composed of Moment invariant and Interior angles of polygons and would be extracted after normalizing the object boundary with respect to size and orientation. Interior angles are extracted from a shape, described using a new polygonal approximation technique. Similarity measurement is done by combining two classifiers, Euclidean distances in Decision theoretic and String matching in Structural methods. In order to investigate the reliability of presented method in presence of noise, the classification results obtained from a hybrid method are compared with those of the Decision theoretic or Structural methods.