Purpose: The purpose of the study is to compare and evaluate Earnings Management in Tunisia and Iraq.   Theoretical framework: Earnings Management is an important topic that has been studied by a significant number of researchers, as well as those who are interested in the accounting profession. Earnings Management has gotten a lot of attention from academics, professionals, and other interested parties in recent years (e.g. Kliestik et al., 2020; Rahman et al., 2021; Gamra &Ellouze, 2021)   Design/methodology/approach: The sample includes ten banks listed on the Bourse of Tunisia and Iraq Stock Exchanges for the year 2017. We have used a model of Kothari et al., (2005) as a tool to measure Earnings Management in both mark

      Detecting and subtracting the Motion objects from backgrounds is one of the most important areas. The development of cameras and their widespread use in most areas of security, surveillance, and others made face this problem. The difficulty of this area is unstable in the classification of the pixels (foreground or background). This paper proposed a suggested background subtraction algorithm based on the histogram. The classification threshold is adaptively calculated according to many tests. The performance of the proposed algorithms was compared with state-of-the-art methods in complex dynamic scenes.

In order for the process of removing pollutants, including dyes, from the aquatic environment to be effective, plant wastes such as banana peels were used as adsorbent surfaces by thermally activating them (ABP) and modifying them with iron oxide nanoparticles (MABP), which were characterized using Fourier transform infrared (FT-IR) and X-ray diffraction (XRD) techniques. They were applied in the field of Janus green (JG) dye adsorption for the batch system and studied the effect of several factors (adsorbent weight, contact time, initial concentration, and temperature). Their data were analyzed kinetically using first- and second-order kinetic models and they were found to follow the second order. Their data were also analyzed thro

In order for the process of removing pollutants, including dyes, from the aquatic environment to be effective, plant wastes such as banana peels were used as adsorbent surfaces by thermally activating them (ABP) and modifying them with iron oxide nanoparticles (MABP), which were characterized using Fourier transform infrared (FT-IR) and X-ray diffraction (XRD) techniques. They were applied in the field of Janus green (JG) dye adsorption for the batch system and studied the effect of several factors (adsorbent weight, contact time, initial concentration, and temperature). Their data were analyzed kinetically using first- and second-order kinetic models and they were found to follow the second order. Their data were also analyzed thro

The m-consecutive-k-out-of-n: F linear and circular system consists of n sequentially connected components; the components are ordered on a line or a circle; it fails if there are at least m non-overlapping runs of consecutive-k failed components. This paper proposes the reliability and failure probability functions for both linearly and circularly m-consecutive-k-out-of-n: F systems. More precisely, the failure states of the system components are separated into two collections (the working and the failure collections); where each one is defined as a collection of finite mutual disjoint classes of the system states. Illustrative example is provided.

Abstract

       The grey system model GM(1,1) is the model of the prediction of the time series and the basis of the grey theory. This research presents the methods for estimating parameters of the grey model GM(1,1) is the accumulative method (ACC), the exponential method (EXP), modified exponential method (Mod EXP) and the Particle Swarm Optimization method (PSO). These methods were compared based on the Mean square error (MSE) and the Mean Absolute percentage error (MAPE) as a basis comparator and the simulation method was adopted for the best of the four methods, The best method was obtained and then applied to real data. This data represents the consumption rate of two types of oils a he

 

This work implements the face recognition system based on two stages, the first stage is feature extraction stage and the second stage is the classification stage. The feature extraction stage consists of Self-Organizing Maps (SOM) in a hierarchical format in conjunction with Gabor Filters and local image sampling. Different types of SOM’s were used and a comparison between the results from these SOM’s was given.

The next stage is the classification stage, and consists of self-organizing map neural network; the goal of this stage is to find the similar image to the input image. The proposal method algorithm implemented by using C++ packages, this work is successful classifier for a face database consist of 20