The ground state densities of unstable neutron-rich 8He and 17B exotic nuclei are studied via the framework of the two-frequency shell model (TFSM) and the binary cluster model (BCM). In TFSM, the single particle harmonic oscillator wave functions are used with two different oscillator size parameters βc and βv where the former is for the core (inner) orbits and the latter is for the valence (halo) orbits. In BCM, the internal densities of the clusters are described by single particle Gaussian wave functions. Shell model calculations for the two valence neutrons in 8He and 17B are performed via the computer code OXBASH. The long tail performance is clearly noticed in the calculated neutron and matter density distributions of these nucl

Coherent density fluctuation model (CDFM) has been used to calculate the
proton momentum distributions (PMD) and elastic electron scattering form factors,
F(q), of the ground state for some even mass nuclei of fp-shell, such as 52Cr, 58Fe and
64Ni nuclei. Both of the PMD and F(q) have been expressed in terms of the weight
function ( ( ) )
2
f x which is determined by means of the charge density
distributions (CDD) of the nuclei and determined from theory and experiment. The
feature of the long-tail behavior at high momentum region of the PMD’s has been
obtained by both the theoretical and experimental weight functions. The calculated
form factors of these nuclei are in reasonable agreement with those of th

In this paper, inelastic longitudinal electron scattering form factors C2 and C4
transitions have been studied in Ti 48,50
and Cr 52,54
nuclei with the aid of shell
model calculations. The core polarization transition density was evaluated by
adopting the shape of Tassie model togther with the derived form of the ground state
two-body charge density distributions (2BCDD's). The following transitions have
been investigated; 0 2 2 2 1 1
   and 0 2 4 2 1 1
   of Ti 48 , 0 3 2 3 1 1
   and
0 3 4 3 1 1
   of Ti 50 , 0 2 2 2 1 1
   and 0 2 4 2 1 1
   of Cr 52 and
0 3 2 3 1 1
   and 0 3 4 3 1 1
   of Cr 54 nuclei. It is fou

HM Al-Dabbas, RA Azeez, AE Ali, IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING, 2023

Cuneiform symbols recognition represents a complicated task in pattern recognition  and image analysis as a result of problems that related to cuneiform symbols like distortion and unwanted objects that associated with applying Binrizetion process like spots and writing lines. This paper aims to present new proposed algorithms to solve these problems for reaching uniform results about cuneiform symbols recognition that related to (select appropriate Binerized method, erased writing lines and spots) based on statistical Skewness measure, image morphology and distance transform concepts. The experiment results show that our proposed algorithms have excellent result and can be adopted

Face recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face ima

The interests toward developing accurate automatic face emotion recognition methodologies are growing vastly, and it is still one of an ever growing research field in the region of computer vision, artificial intelligent and automation. However, there is a challenge to build an automated system which equals human ability to recognize facial emotion because of the lack of an effective facial feature descriptor and the difficulty of choosing proper classification method. In this paper, a geometric based feature vector has been proposed. For the classification purpose, three different types of classification methods are tested: statistical, artificial neural network (NN) and Support Vector Machine (SVM). A modified K-Means clustering algorithm

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog