This paper proposes a novel meta-heuristic optimization algorithm called the fine-tuning meta-heuristic algorithm (FTMA) for solving global optimization problems. In this algorithm, the solutions are fine-tuned using the fundamental steps in meta-heuristic optimization, namely, exploration, exploitation, and randomization, in such a way that if one step improves the solution, then it is unnecessary to execute the remaining steps. The performance of the proposed FTMA has been compared with that of five other optimization algorithms over ten benchmark test functions. Nine of them are well-known and already exist in the literature, while the tenth one is proposed by the authors and introduced in this article. One test trial was shown t

In this paper, we characterize the percolation condition for a continuum secondary cognitive radio network under the SINR model. We show that the well-established condition for continuum percolation does not hold true in the SINR regime. Thus, we find the condition under which a cognitive radio network percolates. We argue that due to the SINR requirements of the secondaries along with the interference tolerance of the primaries, not all the deployed secondary nodes necessarily contribute towards the percolation process- even though they might participate in the communication process. We model the invisibility of such nodes using the concept of Poisson thinning, both in the presence and absence of primaries. Invisibility occurs due to nodes

This research attempted to take advantage of modern techniques in the study of the superstructural phonetic features of spoken text in language using phonetic programs to achieve more accurate and objective results, far from being limited to self-perception and personal judgment, which varies from person to person.
    It should be noted that these phonological features (Nabr, waqf, toning) are performance controls that determine the fate of the meaning of the word or sentence, but in the modern era has received little attention and attention, and that little attention to some of them came to study issues related to the composition or style Therefore, we recommend that more attention should be given to the study of

Among a variety of approaches introduced in the literature to establish duality theory, Fenchel duality was of great importance in convex analysis and optimization. In this paper we establish some conditions to obtain classical strong Fenchel duality for evenly convex optimization problems defined in infinite dimensional spaces. The objective function of the primal problem is a family of (possible) infinite even convex functions. The strong duality conditions we present are based on the consideration of the epigraphs of the c-conjugate of the dual objective functions and the ε-c-subdifferential of the primal objective functions.

This research has discussed the origins of ESP, addressed key notions about ESP and examined issues in ESP syllabus design. The content of the paper was determined by a need identified based on my  experience as an ESL instructor designing and delivering the content-based language program - Language Preparation for the Cadets and Employment in the Iraqi  College of Police . These issues, where possible, have been supported by current and pertinent academic literature. It is my sincerest hope that these observations will lend insight into the challenges facing the ESL instructor acting as ESP syllabus developer.

Assume that G is a finite group and X = tG where t is non-identity element with t3 = 1. The simple graph with node set being X such that a, b ∈ X, are adjacent if ab-1 is an involution element, is called the A4-graph, and designated by A4(G, X). In this article, the construction of A4(G, X) is analyzed for G is the twisted group of Lie type 3D4(3).

One of the significant stages in computer vision is image segmentation which is fundamental for different applications, for example, robot control and military target recognition, as well as image analysis of remote sensing applications. Studies have dealt with the process of improving the classification of all types of data, whether text or audio or images, one of the latest studies in which researchers have worked to build a simple, effective, and high-accuracy model capable of classifying emotions from speech data, while several studies dealt with improving textual grouping. In this study, we seek to improve the classification of image division using a novel approach depending on two methods used to segment the images. The first

     A Spectroscopic study has been focused in this article to study one of the main types of active galaxies which are quasars, and to be more precise this research focuses on studying the correlation between the main engine of Quasi-Stellar Objects (QSO), the central black hole mass (SMBH) and other physical properties (e.g. the star formation rate (SFR)). Twelve objects have been randomly selected for “The Half Million Quasars (HMQ) Catalogue” published in 2015 and the data collected from Salon Digital Sky survey (SDSS) Dr. 16. The redshift range of these galaxies were between (0.05 – 0.17). The results show a clear linear proportionality between the SMBH and the SFR, as well as direct proportional between the luminosit

Building a system to identify individuals through their speech recording can find its application in diverse areas, such as telephone shopping, voice mail and security control. However, building such systems is a tricky task because of the vast range of differences in the human voice. Thus, selecting strong features becomes very crucial for the recognition system. Therefore, a speaker recognition system based on new spin-image descriptors (SISR) is proposed in this paper. In the proposed system, circular windows (spins) are extracted from the frequency domain of the spectrogram image of the sound, and then a run length matrix is built for each spin, to work as a base for feature extraction tasks. Five different descriptors are generated fro