Dyslexia is a distinct learning disability that is distinguished by difficulties in decoding and reading fluency, spelling as well as other cognitive skills. This research is conducted to assist learning disability by improving the skills required for children with learning disability in reading and spelling using the assistive technology AVA interactive whiteboard. The approach is by strengthening the micro skills required to improve the fundamental skills such as reading, and spelling thus facilitating the learning process. Tactile and visual modalities are the main focus in the use of the interactive whiteboard. This study aims to develop an interactive whiteboard learning prototype for students with dyslexia. A total of six children from 3 demographics (Chinese, Malay, and Indian) participated in the study. Activities improving the four micro skills were introduced in normal whiteboard and the children were exposed to the prototype AVA interactive whiteboard. Responses using Interactive whiteboard were different when compared to their performance with the normal whiteboard. The analyzed results indicated showed improvements in three of the micro skills using IWB

This systematic review investigates the impact of the COVID-19 pandemic on cybersecurity by examining the rise of cyberattacks targeting financial institutions. By analyzing over 60 sources including academic research, government reports, and security company data, the review reveals a nearly two-fold increase in cybercrimes targeting financial institutions during the pandemic compared to pre-pandemic levels. Phishing attacks are identified as the most prevalent form of cybercrime within this context. The review delves further by analyzing the differences between phishing and other cyberattacks during COVID-19. Based on the results and observations presented in this paper, the analysis will contribute to enriching strategies to address the growing threat of cybercrime in the financial sector

Background and aims

In recent years, the education sector in Libya have been affected by wars and political fluctuations, impacting the quality and availability of education. There have been challenges in providing the necessary educational resources, as well as in training and qualifying teachers. E-learning, it can be an effective tool in improving education under difficult circumstances. Online education can provide opportunities for students to access educational resources easily, and can contribute to enhancing communication between teachers and students. E-learning can also play a role in developing students’ technology skills, which is important in our modern era. In 2019, the Faculty of Science at the University of Gharyan partially implemented online learning as a response to COVID-19 pandemic, and this program continued for two academic terms. Therefore, the paper aims to highlights both the advantages and limitations of E-learning within the environment of university education in Libya

A new robust blind image watermarking method based on lifting wavelet transform (LWT), and Schur decomposition in combination with discrete cosine transform (DCT), and entropy analysis for copyright protection of multimedia information is introduced in this paper. At first, the 2-level LWT is applied to the original image to improve the invisibility of the watermarking method and then the high-frequency sub-band of 2-level LWT is decomposed by DCT. Then, the DCT coefficients are divided into 4×4 non-overlapping blocks. After that, Schur decomposition applies to each selected block, while the first row, the first column element of the upper triangular matrix is used to embed the watermark. For evaluation, the invisibility and robustness of the proposed watermarking method, the peak signal to noise ratio (PSNR), and normalized cross-correlation (NC) are used to measure the quality and the ability of the proposed watermarking method to be robust against signal processing operations and geometric attacks. Experimental results have demonstrated that the proposed method is achieved a very good tradeoff between invisibility and robustness. The comparisons with other scheme have shown that the proposed digital watermarking scheme has a superior performance in terms of invisibility than other. 

In this paper, a novel robust blind grayscale image digital watermarking scheme is introduced based on lifting wavelet transform (LWT) in combination with discrete cosine transform (DCT), Hessenberg decomposition, and entropy analysis for copyright protection of multimedia information. At first, the two levels of LWT are applied to the host grayscale image to improve the imperceptibility of the watermarking scheme and then the high-frequency sub-band of the 2-level of LWT is decomposed by DCT. Next, the DCT coefficients are divided into 4× 4 non-overlapping blocks. After that, Hessenberg decomposition performs on each selected block, whereas the first row, first column element of the upper Hessenberg matrix is utilized to hide the watermark. To evaluate the imperceptibility and robustness of the proposed digital watermarking scheme, the peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NC) are utilized to measure the quality and the ability of the proposed watermarking scheme to robust against signal processing operations and geometric attacks. Experimental and analysis results have demonstrated that the proposed scheme is achieved a very good tradeoff between imperceptibility and robustness. The comparison with other scheme have shown that the proposed digital watermarking schemes have a superior performance in terms of imperceptibility and robustness than other

The network connectivity is often lost during some situations such as in war zones, battle fields or in regard to natural disasters like floods or earthquakes. Therefore, the connection becomes difficult for rescuing teams to locate people. At the same time, mobile nodes are difficult to be tracked/located. Therefore, flying ad-hoc networks (FANETs) is the solution which provides a way to tackle this situation without the need for any central infrastructure with the use of flying objects (nodes). FANTEs are frequently formed of tiny unmanned aerial vehicles (UAVs) which are flexible, affordable, and rapid to deploy. UAV as a router and without complex hardware deployment, provides an effective real-time communication solution for multiple UAV systems. FANETs can also be used to provide connectivity in such areas in which flying nodes are utilized. In this paper, we discuss the importance of the FANETs and present a routing protocol comparison that depends on the link budget to identify the optimized packet route for the data to be transmitted. The simulation design results show that a throughput enhancement of 0.62Gbps is achieved in comparison to the distance-based routing protocol. The results also confirm the efficiency of the optimized link state routing (OSLR) protocol over the other. The performance of FANETs can be optimized further by using the OLSR protocol.

Face recognition is a biometric technique that can be used for a variety of purposes, such as national security, access control, identity fraud, banking, and finding missing children. Faces are highly dynamic and facial features are not always easily extracted, which can lead to discarding textural information like the smoothness of faces, a hairstyle that, might contain strong identity information. In addition, brightness, scale, and facial expressions play a significant role in the face-recognizing process. Therefore, face recognition is considered as a difficult problem. To figure out this problem effective methods using databases techniques are needed. This paper describes face recognition methods and their structure. Based on Wen Yi Zhao and Rama Chellappa work the face recognition methods are divided into three groups: a holistic approach, feature-based approach, and hybrid approach, where Principal Component Analysis PCA, a holistic approach method, is presented as a mathematical technique that can assist the process of face recognition. Also, the paper shows how the PCA is used to extract facial features by removing the principal components of the available multidimensional data.

Counting the number of people in many estimation systems, such as still images or video frames, is a buoyant research area that is challenging in the field of computer vision. It plays a considerable role in a variety of applications, such as security, management, education, and commerce. The purpose of this paper is to suggest a system to estimate the number of people in digital still images based on the Face Detection method. This system composed of two parts: face detection and counting of detected faces. In the detection step, the Skin Color Face Detection method was applied on the input of a digital still image. In the counting part, the obtained detected faces by the Skin Color Face Detection method have counted to estimate the number of people in an input color image with simple software and simple low-cost hardware. The skin color face detection algorithm was tested using 133 images from the People Image Groups dataset, which contains about 2573 color images of people, to test the proposed system. based on the obtained results, the best precision achieved of the proposed Skin Color face detection algorithm was 85%

Face recognition is a biometric technique that can be used for a variety of purposes, such as national security, access control, identity fraud, banking, and finding missing children. Faces are highly dynamic and facial features are not always easily extracted, which can lead to discarding textural information like the smoothness of faces, a hairstyle that, might contain strong identity information. In addition, brightness, scale, and facial expressions play a significant role in the face-recognizing process. Therefore, face recognition is considered as a difficult problem. To figure out this problem effective methods using databases techniques are needed. This paper describes face recognition methods and their structure. Based on Wen Yi Zhao and Rama Chellappa work the face recognition methods are divided into three groups: a holistic approach, feature-based approach, and hybrid approach, where Principal Component Analysis PCA, a holistic approach method, is presented as a mathematical technique that can assist the process of face recognition. Also, the paper shows how the PCA is used to extract facial features by removing the principal components of the available multidimensional data.