Corona Virus Disease-2019 (COVID-19) is a novel virus belongs to the corona virus's family. It spreads very quickly and causes many deaths around the world. The early diagnosis of the disease can help in providing the proper therapy and saving the humans' life. However, it founded that the diagnosis of chest radiography can give an indicator of coronavirus. Thus, a Corner-based Weber Local Descriptor (CWLD) for COVID-19 diagnostics based on chest X-Ray image analysis is presented in this article. The histogram of Weber differential excitation and gradient orientation of the local regions surrounding points of interest are proposed to represent the patterns of the chest X-Ray image. Support Vector Machine (SVM) and Deep Belief Network (DBN)

Acute appendicitis is the most common surgical abdominal emergency. Its clinical diagnosis remains a challenge to surgeons, so different imaging options were introduced to improve diagnostic accuracy. Among these imaging modality choices, diagnostic medical sonography (DMS) is a simple, easily available, and cost effective clinical tool. The purpose of this study was to assess the accuracy of DMS, in the diagnosis of acute appendicitis compared to the histopathology report, as a gold standard. Between May 2015 and May 2016, 215 patients with suspected appendicitis were examined with DMS. The DMS findings were recorded as positive and negative for acute appendicitis and compared with the histopathological results, as a gold standard

This paper describes a practical study on the impact of learning's partners, Bluetooth Broadcasting system, interactive board, Real – time response system, notepad, free internet access, computer based examination, and interaction classroom, etc, had on undergraduate student performance, achievement and involving with lectures. The goal of this study is to test the hypothesis that the use of such learning techniques, tools, and strategies to improve student learning especially among the poorest performing students. Also, it gives some kind of practical comparison between the traditional way and interactive way of learning in terms of lectures time, number of tests, types of tests, student's scores, and student's involving with lectures

Wireless Body Area Sensor Networks (WBASNs) have garnered significant attention due to the implementation of self-automaton and modern technologies. Within the healthcare WBASN, certain sensed data hold greater significance than others in light of their critical aspect. Such vital data must be given within a specified time frame. Data loss and delay could not be tolerated in such types of systems. Intelligent algorithms are distinguished by their superior ability to interact with various data systems. Machine learning methods can analyze the gathered data and uncover previously unknown patterns and information. These approaches can also diagnose and notify critical conditions in patients under monitoring. This study implements two s

Steganography is the art of secret communication. Its purpose is to hide the presence of information, using, for example, images as covers. The frequency domain is well suited for embedding in image, since hiding in this frequency domain coefficients is robust to many attacks. This paper proposed hiding a secret image of size equal to quarter of the cover one. Set Partitioning in Hierarchal Trees (SPIHT) codec is used to code the secret image to achieve security. The proposed method applies Discrete Multiwavelet Transform (DMWT) for cover image. The coded bit stream of the secret image is embedded in the high frequency subbands of the transformed cover one. A scaling factors ? and ? in frequency domain control the quality of the stego

Some problems want to be solved in image compression to make the process workable and more efficient. Much work had been done in the field of lossy image compression based on wavelet and Discrete Cosine Transform (DCT). In this paper, an efficient image compression scheme is proposed, based on a common encoding transform scheme; It consists of the following steps: 1) bi-orthogonal (tab 9/7) wavelet transform to split the image data into sub-bands, 2) DCT to de-correlate the data, 3) the combined transform stage's output is subjected to scalar quantization before being mapped to positive, 4) and LZW encoding to produce the compressed data. The peak signal-to-noise (PSNR), compression ratio (CR), and compression gain (CG) measures were used t

Electrocardiogram (ECG) is an important physiological signal for cardiac disease diagnosis. With the increasing use of modern electrocardiogram monitoring devices that generate vast amount of data requiring huge storage capacity. In order to decrease storage costs or make ECG signals suitable and ready for transmission through common communication channels, the ECG data
volume must be reduced. So an effective data compression method is required. This paper presents an efficient technique for the compression of ECG signals. In this technique, different transforms have been used to compress the ECG signals. At first, a 1-D ECG data was segmented and aligned to a 2-D data array, then 2-D mixed transform was implemented to compress the

      TV medium derives its formal shape from the technological development taking place in all scientific fields, which are creatively fused in the image of the television, which consists mainly of various visual levels and formations. But by the new decade of the second millennium, the television medium and mainly (drama) became looking for that paradigm shift in the aesthetic formal innovative fields and the advanced expressive performative fields that enable it to develop in treating what was impossible to visualize previously. In the meantime, presenting what is new and innovative in the field of unprecedented and even the familiar objective and intellectual treatments. Thus the TV medium has sought for work