Background: Oral SCC is a complex malignancy where environmental factors, viral infections and genetic alterations most likely interact, and thus give rise to the malignant condition. The HSP70 play a direct role in apoptosis inhibition by aligning the improved integrity of a cell’s proteins with the improved chances of that particular cell’s survival.P21 gene produces p21 protein which is a potent cyclin-dependent kinase inhibitor that plays a significant role in carcinogenesis. The aims of the study were to evaluate and compare the immun-histochemical expression of the HSP70 and cell cycle protein p21in NOM, OED, and OSCC. Correlate both marker expressions with each other. Materials and methods: Forty six formalin-fixed, par

This report explores emerging techniques to boost multimedia transfer effectiveness, given the escalating need for improved quality and performance in multimedia interactions. The analysis involves a thorough literature assessment and comparison of present strategies to pinpoint key tendencies and propose novel approaches. The methodology involves examining recent technological enhance ments in video coding standards, quality appraisal methods, and compression tech niques. Specific domains investigated comprise firmware component architectures, 4D indexing structures, and iterative filtering frameworks. The study in addition weighs tradeoffs between video quality, encoding intricacy, and bitrate demands. Key determinations consist of

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

In this paper we estimate the coefficients and scale parameter in linear regression model depending on the residuals are of type 1 of extreme  value distribution for the largest values . This can be regard as an improvement for the studies with the smallest values . We study two estimation methods ( OLS  & MLE ) where we resort to Newton – Raphson (NR) and Fisher Scoring methods to get MLE estimate because the difficulty of using the usual approach with MLE . The relative efficiency criterion is considered beside to the statistical inference procedures for the extreme value regression model of type 1 for largest values . Confidence interval , hypothesis testing for both scale parameter and regression coefficients

A set newly complexes with the general formula [M(L)Cl2] are resulting from the reaction of a new schiff base ligand [Ethyl (6R,7R)-7-((E)-2-((2-ethoxy-2- oxoethoxy)imino)-2-(2-(((E)-4-nitrobenzylidene) amino) thiazol -4- yl) acetamido) -8- oxo -3- vinyl -5- thia -1-aza bicyclo [4. 2.0] oct -2- ene -2- carboxylate] (L). This ligand was derived from the reaction of the two substances 4-nitrobenzaldehyde and precursor (P). Reaction the ligand with metal ions M= Mn(II), Co(II), Ni(II), Cu(II) and Cd(II) afforded new complexes which are characterized by FT-IR and Electronic Spectra. These measurements indicate that the complexes have a tetrahedral geometry. The Penicillin-Binding Protein 3 (PBP3) of Staphylococcus aureus and the target protein

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

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