Early and accurate detection of COVID-19 from chest computed tomography (CT) scans are becoming essential for effective clinical decision-making and disease control. This study is proposing a robust deep learning framework that integrates a convolutional self-attention network (CSAN), gamma correction for image enhancement, and a voting-based ensemble classifier to improving diagnostic performance. The model is being evaluated on a dataset of 2,271 CT images and is achieving an accuracy of 95.12%, sensitivity of 97.25%, specificity of 98.11%, F1-score of 96.46%, and area under the curve (AUC) of 0.977. Experimental results are demonstrating that the proposed method significantly surpasses baseline models, including standalone CSAN,

Although text document images authentication is difficult due to the binary nature and clear separation between the background and foreground but it is getting higher demand for many applications. Most previous researches in this field depend on insertion watermark in the document, the drawback in these techniques lie in the fact that changing pixel values in a binary document could introduce irregularities that are very visually noticeable. In this paper, a new method is proposed for object-based text document authentication, in which I propose a different approach where a text document is signed by shifting individual words slightly left or right from their original positions to make the center of gravity for each line fall in with the m

In this work a fragile watermarking scheme is presented. This scheme is applied to digital color images in spatial domain. The image is divided into blocks, and each block has its authentication mark embedded in it, we would be able to insure which parts of the image are authentic and which parts have been modified. This authentication carries out without need to exist the original image. The results show the quality of the watermarked image is remaining very good and the watermark survived some type of unintended modification such as familiar compression software like WINRAR and ZIP

The research dealt with the study of the element of time in the cinematic language and the role it plays in creating the aesthetic meanings in the documentary film by means of the various structural constructions with which time is constructed. The research included the introduction and the problem of research, which was represented by the following question: (Did the documentary film adopt the values of time formats in the fiction film or it created for itself special linguistic formulations and models which achieve the aesthetic concept?). It also included the importance of research, the need for it and its goal in addition to clarifying the limits of research.The theoretical framework included three sections the first of which is the

Paul Auster's City of Glass is here singled out as representative of the writer's The New
York Trilogy. All throughout his novelistic career, Auster has been working on a pseudothesis
that adheres to a certain aesthetic of disappearance. The study engages this Austerian
aesthetic apropos of certain theoretical stretches such as the Emersonian "Not Me", the
Thoreauvian "interval" or "nowhere", the Deleuzian "nomadic trajectory", the Derridian
"grammè" or "specter", and the Baudrillardian "disappearance". The city of the novel's titling
is here seen as the trope of all that which has already disappeared, and hence it is seen as the
space (mise en scène) where the perfect crime of the murder of the real is to be thoro

Human cytomegalovirus (HCMV) infection is ubiquitous and successfully reactivated in patients with immune dysfunction as in patient with multiple myeloma (MM), causing a wide range of life-threatening diseases. Early detection of HCMV and significant advances in MM management has amended patient outcomes and prolonged survival rates.

The rapid development of Internet of Things (IoT) devices and their increasing numbers have caused a tremendous increase in network traffic and a wider range of cyber-attacks. This growing trend has complicated the detection process for traditional intrusion detection systems and heightened the challenges faced by these devices, such as imbalanced and large training data. This study presents a cohesive methodology of a series of intelligent techniques to prepare clean and balanced data for training the first (core) layer of a robust hierarchical intrusion detection system. The methodology was built by cleaning and compressing the data using an Autoencoder and preparing a strong latent space for balancing using a hybrid method that combines

Background: Molars and premolars are considered as the most vulnerable teeth of caries attack, which is related to the morphology of their occlusal surfaces along with the difficulty of plaque removal. different methods were used for early caries detection that provide sensitive, accurate preoperative diagnosis of caries depths to establish adequate preventive measures and avoid premature tooth treatment by restoration. The aim of the present study was to evaluate the clinical sensitivity and specificity rates of DIAGNOdent and visual inspection as opposed to the ICDAS for the detection of initial occlusal caries in noncavitated first permanent molars. Materials and Methods: This study examined 139 occlusal surface of the first permanent

A procedure for the mutual derivatization and determination of thymol and Dapsone was developed and validated in this study. Dapsone was used as the derivatizing agent for the determination of thymol, and thymol was used as the derivatizing agent for the determination of Dapsone. An optimization study was performed for the derivatization reaction; i.e., the diazonium coupling reaction. Linear regression calibration plots for thymol and Dapsone in the direct reaction were constructed at 460 nm, within the concentration range of 0.3-7 μg ml-1 for thymol and 0.3-4 μg ml-1 for Dapsone, with limits of detection 0.086 and 0.053 μg ml-1, respectively. Corresponding plots for the cloud point extraction of thymol and Dapsone were constructed

A simple, low cost and rapid flow injection turbidimetric method was developed and validated for mebeverine hydrochloride (MBH) determination in pharmaceutical preparations. The developed method is based on forming of a white, turbid ion-pair product as a result of a reaction between the MBH and sodium persulfate in a closed flow injection system where the sodium persulfate is used as precipitation reagent. The turbidity of the formed complex was measured at the detection angle of 180° (attenuated detection) using NAG dual&Solo (0-180°) detector which contained dual detections zones (i.e., measuring cells 1 & 2). The increase in the turbidity of the complex was directly proportional to the increase of the MBH concentration