In recent years, with the rapid development of the current classification system in digital content identification, automatic classification of images has become the most challenging task in the field of computer vision. As can be seen, vision is quite challenging for a system to automatically understand and analyze images, as compared to the vision of humans. Some research papers have been done to address the issue in the low-level current classification system, but the output was restricted only to basic image features. However, similarly, the approaches fail to accurately classify images. For the results expected in this field, such as computer vision, this study proposes a deep learning approach that utilizes a deep learning algorithm.

Objectives. This study was carried out to quantitatively evaluate and compare the sealing ability of Endoflas by using differentobturation techniques. Materials and Methods. After 42 extracted primary maxillary incisors and canines were decoronated, theircanals were instrumented with K files of size ranging from #15 to #50. In accordance with the obturation technique, the sampleswere divided into three experimental groups, namely, group I: endodontic pressure syringe, group II: modified disposable syringe,and group III: reamer technique, and two control groups. Dye extraction method was used for leakage evaluation. Data wereanalyzed using one-way ANOVA and Dunnett’s T3 post hoc tests. The level of significance was set at p<0:05. Results.

The study showed significant differences between the average weight lens and the average amount protein in the lens between that Kestrel Falco tinnunculus L. and the Collared Dove Streptopelia decaocto F. , also the study electrical migration of lens proteins having one bundle of crystalline –? in Kestrel compared with three bundles in Collared Dove, two bundles of crystalline – ? in both , and crystalline – ? appeared as one bundle in both birds.

Introduction: Surgical removal of impacted lower wisdom teeth is one of the most commonly performed proce- dures in oral and maxillofacial surgery. Objectives: This study aimed to compare the operative and post-operative outcomes of 2 flap designs (limited vs. extended 2-sided flap) in surgical extractions of lower third molars (M3) regarding fully impacted teeth. Material and methods: A prospective, randomized, controlled split-mouth trial was designed, and included 27 patients with bilateral (symmetrically positioned) impacted M3. Limited 2-sided flap was performed on one side (group 1) and extended 2-sided flap on the other side (group 2). The effect of flap design on intra-operative (visibility, accessibility, and duration of surgery) a

  Amidst the changes resulting from the subject matter of expression in art. The necessity of searching for the expressive features of thought that leaves different imprints with aesthetic features and values which called for re-modifying the expressive vision of contemporary drawings. Therefore, this research has been concerned with the study of (abstract expressive features in the drawings of (Serwan Baran) and (Eric Barto) - a comparative study), and the research includes four chapters. The first chapter is devoted to explaining the research problem, its importance, need, purpose, and limits, then determining the most important terms mentioned in it. Where the research problem dealt with the subject of abstract expressive feature

This research aims to test the relationship between "relational leadership as an independent variable and organizational energy as a dependent variable. The current research variables are among the recent and important variables for the development of organizations, and for the purpose of explaining the relationship and influence between the variables, a set of goals has been formulated, including providing the interested and scientific and theoretical information explaining the nature of the variables The research, and the extent to which its causes are reflected in the research sample to increase the interest of the research organization’s organization and make it more appropriate to the required performance in light of a cha

A new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification