Data Mining - Concepts and Techniques.

being used in many other applications to explicitly declare the knowledge embedded in them. However, not only are ontologies useful for applications in which knowledge plays a key role, but they can also trigger a major change in current Web contents. This change is leading to the third generation of the Web—known as the Semantic Web—which has been defined as “the conceptual structuring of the Web in an explicit machine-readable way.”1 This definition does not differ too much from the one used for defining an ontology: “An ontology is an explicit, machinereadable specification of a shared conceptualization.”2 In fact, new ontology-based applications and knowledge architectures are developing for this new Web. A common claim for all of these approaches is the need for languages to represent the semantic information that this Web requires—solving the heterogeneous data exchange in this heterogeneous environment. Here, we don’t decide which language is best of the Semantic Web. Rather, our goal is to help developers find the most suitable language for their representation needs.

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Ontology languages for the Semantic Web

A proposed paradigm for smart learning environment based on semantic web

The purpose of this study is to define Web 3.0 and discuss the underlying technologies, identify new opportunities and highlight potential challenges that are associated with the evolution to Web 3.0 technologies.,A non-empirical study reviewing papers published in accredited research journals, articles and whitepapers and websites was conducted. To add scientific rigour to a literature review, a four-stage approach, as suggested by Sylvester et al. (2011), was used.,The World Wide Web (henceforth referred to as the Web) is recognised as the fastest growing publication medium of all time. To stay competitive, it is crucial to stay up to date with technological trends. The Web matures in its own unique way. From the static informative characteristics of Web 1.0, it progressed into the interactive experience Web 2.0 provides. The next phase of Web evolution, Web 3.0, is already in progress. Web 3.0 entails an integrated Web experience where the machine will be able to understand and catalogue data in a manner similar to humans. This will facilitate a world wide data warehouse where any format of data can be shared and understood by any device over any network. The evolution of the Web will bring forth new opportunities and challenges. Opportunities identified can mainly be characterised as the autonomous integration of data and services which increase the pre-existing capabilities of Web services, as well as the creation of new functionalities. The challenges mainly concern unauthorised access and manipulation of data, autonomous initiation of actions and the development of harmful scripts and languages.,The findings will assist data managers to identify future opportunities while considering negative impacts and understanding the underlying technologies associated with the structure and storage of electronic information. The research will assist anyone in the data and information management industry to identify opportunities and mitigate risk.,Many organisations were caught off guard by the evolution of the Web to Web 2.0. Organisations, and in particular anyone in the data and information management industry, need to be ready and acquire knowledge about the opportunities and challenges arising from Web 3.0 technologies.

Defining Web 3.0: opportunities and challenges

Universities worldwide are pausing in an attempt to contain COVID-19's spread. In February 2019, universities in China took the lead, cancelling all in-person classes and switching to virtual classrooms, with a wave of other institutes globally following suit. The shift to online platform poses serious challenges to medical education so that understanding best practices shared by pilot institutes may help medical educators improve teaching. Provide 12 tips to highlight strategies intended to help on-site medical classes moving completely online under the pandemic. We collected 'best practices' reports from 40 medical schools in China that were submitted to the National Centre for Health Professions Education Development. Experts' review-to-summary cycle was used to finalize the best practices in teaching medical students online that can benefit peer institutions most, under the unprecedented circumstances of the COVID-19 outbreak. The 12 tips presented offer-specific strategies to optimize teaching medical students online under COVID-19, specifically highlighting the tech-based pedagogy, counselling, motivation, and ethics, as well as the assessment and modification. Learning experiences shared by pilot medical schools and customized properly are instructive to ensure a successful transition to e-learning.

https://www.tandfonline.com/doi/pdf/10.1080/10872981.2020.1854066

Twelve tips for teaching medical students online under COVID-19.

Research works in the field of E-Learning are represented by a broad spectrum of applications, ranged from virtual classrooms to remote courses or distance learning. Web-based courses offer obvious advantages for learners by making access to educational resource very fast, just-in-time and relevance, at any time or place. In this paper, based on our previous work, we present the Semantic Web-Based model for our e-learning system. In addition we present an approach for developing a Semantic Web-based e-learning system, which focus on the RDF data model and OWL ontology language. We demonstrate the effectiveness of this approach through several experiments using different type of courses taught in Qatar university. The feedbacks of both teachers and students were highly promising.

http://www.ijcis.info/International%20Journal%20of%20Computing%20and%20Information%20Sciences_files/Vol4N2_IMCL/pp63-71.pdf

E-Learning Model Based On Semantic Web Technology

E-learning is being increasing viewed as an important activity in the field of distance and continuing education. Web-based courses offer obvious advantages for learners by making access to educational resource very fast, just-in-time and relevant, at any time or place. In this study, based on our previous work, we present a framework for our web-based e-learning system using the Semantic Web technology. In addition we present an approach for implementing a Semantic Web-based e-learning system, which focus on the RDF data model, OWL ontology language and RAP for parsing RDF documents. Also the use of RAP – a Semantic Web toolkit for developing our application is discussed in more details.

/pdf/a-web-based-e-learning-system-using-semantic-web-framework-37krr5n5tj.pdf

A Web-Based E-Learning System Using Semantic Web Framework

DNA microarrays allow simultaneous measurements of expression levels for a large number of genes across a number of different experimental conditions (samples). The algorithms for mining association rules are used to reveal biologically relevant associations between different genes under different experimental samples. This paper presents a new column-enumeration based method algorithm (abbreviated by MCR-Miner) for mining maximal high confidence association rules for up/down-expresse d genes. MCR-Miner algorithm uses an efficient maximal association rules tree data structure (abbreviated by MAR-Tree). MAR-tree enumerates (lists) all genes with their binary representations, the binary r epresentation of a gene saves the status (normal, up, and down- expressed) of a gene in all experiments. The binary representation has many advantages, scan the dataset only once, the measurements of confidences for association rules are made in one step, and it makes MCR-Miner algorithm easily finds all maximal high confidence association rules. In the experimental results on a real microarray data sets, MCR-Miner algorithm attained very promising results and outperformed other counterparts.

/pdf/mcr-miner-maximal-confident-association-rules-miner-1eu6s1xx2l.pdf

MCR-Miner: Maximal Confident Association Rules Miner Algorithm for Up/Down-Expressed Genes

Steganography is a process of concealing the secret information into cover object such as text, audio, image, and video. In this paper, we propose a new treatment of Cuckoo Search (CS) algorithm to solve the problem of video steganography. CS is a metaheuristic search algorithm which was recently devolved by Xin-She Yang and Suash Deb in 2009, inspired by the cuckoo bird breeding behavior. The suggested algorithm is based on taking secret data byte by byte. The bits of each byte are arranged to obtain five different forms. The next step focuses on searching about the best carrier pixel in the cover frame. The best pixel is determined using Euclidian distance which evaluate the similarity between the pixels and different byte forms. The random move from pixel to another is achieved using Levy flight random walk. Finally, the suitable carrier pixel is detected and embedded in its RGB components using the 3-3-2 Least Significant Bit (LSB) replacement technique. In addition, each secret image's color component is embedded separately into a selected cover video's frame. Results show that CS is superior to Genetic Algorithm (GA) and the base technique in term of Peak Signal to Noise Ratio (PSNR).

/pdf/optimized-video-steganography-using-cuckoo-search-algorithm-2gwpqoqfio.pdf

Optimized video steganography using Cuckoo Search algorithm

Distance matrix has diverse usage in different research areas. Its computation is typically an essential task in most bioinformatics applications, especially in multiple sequence alignment. The gigantic explosion of biological sequence databases leads to an urgent need for accelerating these computations. DistVect algorithm was introduced in the paper of Al-Neama et al. (in press) to present a recent approach for vectorizing distance matrix computing. It showed an efficient performance in both sequential and parallel computing. However, the multicore cluster systems, which are available now, with their scalability and performance/cost ratio, meet the need for more powerful and efficient performance. This paper proposes DistVect1 as highly efficient parallel vectorized algorithm with high performance for computing distance matrix, addressed to multicore clusters. It reformulates DistVect1 vectorized algorithm in terms of clusters primitives. It deduces an efficient approach of partitioning and scheduling computations, convenient to this type of architecture. Implementations employ potential of both MPI and OpenMP libraries. Experimental results show that the proposed method performs improvement of around 3-fold speedup upon SSE2. Further it also achieves speedups more than 9 orders of magnitude compared to the publicly available parallel implementation utilized in ClustalW-MPI.

/pdf/an-improved-distance-matrix-computation-algorithm-for-479tqrni0v.pdf

Fayed F. M. Ghaleb

Papers

E-Learning Model Based On Semantic Web Technology

A Web-Based E-Learning System Using Semantic Web Framework

MCR-Miner: Maximal Confident Association Rules Miner Algorithm for Up/Down-Expressed Genes

Optimized video steganography using Cuckoo Search algorithm

An improved distance matrix computation algorithm for multicore clusters.