The concealment of data has emerged as an area of deep and wide interest in research that endeavours to conceal data in a covert and stealth manner, to avoid detection through the embedment of the secret data into cover images that appear inconspicuous. These cover images may be in the format of images or videos used for concealment of the messages, yet still retaining the quality visually. Over the past ten years, there have been numerous researches on varying steganographic methods related to images, that emphasised on payload and the quality of the image. Nevertheless, a compromise exists between the two indicators and to mediate a more favourable reconciliation for this duo is a daunting and problematic task. Additionally, the current

Compression for color image is now necessary for transmission and storage in the data bases since the color gives a pleasing nature and natural for any object, so three composite techniques based color image compression is implemented to achieve image with high compression, no loss in original image, better performance and good image quality. These techniques are composite stationary wavelet technique (S), composite wavelet technique (W) and composite multi-wavelet technique (M). For the high energy sub-band of the 3 rd level of each composite transform in each composite technique, the compression parameters are calculated. The best composite transform among the 27 types is the three levels of multi-wavelet transform (MMM) in M technique wh

A new technique for embedding image data into another BMP image data is presented. The image data to be embedded is referred to as signature image, while the image into which the signature image is embedded is referred as host image. The host and the signature images are first partitioned into 8x8 blocks, discrete cosine transformed “DCT”, only significant coefficients are retained, the retained coefficients then inserted in the transformed block in a forward and backward zigzag scan direction. The result then inversely transformed and presented as a BMP image file. The peak signal-to-noise ratio (PSNR) is exploited to evaluate the objective visual quality of the host image compared with the original image.

One of the most serious health disasters in recent memory is the COVID-19 epidemic. Several restriction rules have been forced to reduce the virus spreading. Masks that are properly fitted can help prevent the virus from spreading from the person wearing the mask to others. Masks alone will not protect against COVID-19; they must be used in conjunction with physical separation and avoidance of direct contact. The fast spread of this disease, as well as the growing usage of prevention methods, underscore the critical need for a shift in biometrics-based authentication schemes. Biometrics systems are affected differently depending on whether are used as one of the preventive techniques based on COVID-19 pandemic rules. This study provides an

These factors, as well as disasters and wars that the Arab homeland exposed to and still under this exposure, in addition to the severe stress upon.

Iraqi Academics have been subjected to direct consequences of 2003 war. Exact figures are not known. It was mentioned. Many people were killed, sacked from their academics jobs, threatened to leave directly or indirectly their jobs, houses, and Iraq. Others have been imprisoned, tortured, or kidnapped / threatened to be kidnapped etc. Their families have been subjected to similar traumatic experiences or threats. All theses stressors may have lead to Post Traumatic (PTSD) Experiences. These Traumatic experiences have lead to Dislocation, relocation and Immigration etc. All sectors of

In this article, Convolution Neural Network (CNN) is used to detect damage and no damage images form satellite imagery using different classifiers. These classifiers are well-known models that are used with CNN to detect and classify images using a specific dataset. The dataset used belongs to the Huston hurricane that caused several damages in the nearby areas. In addition, a transfer learning property is used to store the knowledge (weights) and reuse it in the next task. Moreover, each applied classifier is used to detect the images from the dataset after it is split into training, testing and validation. Keras library is used to apply the CNN algorithm with each selected classifier to detect the images. Furthermore, the performa