Abstract--Being a growing problem, plagiarism is generally defined as "literary theft" and "academic dishonesty" in the literature, and it is really has to be well-informed on this topic to prevent the problem and stick to the ethical principles. This paper presents a survey on plagiarism detection systems, a summary of several plagiarism types, techniques, and algorithms is provided. Common feature of deferent detection systems are described. At the end of this paper authors propose a web enabled system to detect plagiarism in documents , code and images, also this system could be used in E-Learning, E-Journal, and E-Business.

Due to the rapid increase of internet-based data, there is urgent need for a robust intelligent documents security mechanism. Although there are many attempts to build a plagiarism detection system in natural language documents, the unlimited variation and different writing styles of each character in Arabic documents make building such systems challenging. Based on its position in a word, the same Arabic letter can be written three different ways, which makes the handwritten character recognition a cumbersome process. This article proposes an intelligent unsupervised model to detect plagiarism in these documents called ASTAP. First, a handwritten Arabic character recognition system is proposed using the Grey Wolf Optimization (GWO) algorithm. Then, a modified Abstract Syntax Tree (AST) is used to match the contents of the Arabic documents to detect any similarity. Compared to the state-of-the-art methods, ASTAP improves the effectiveness of the plagiarism detection in terms of the matched similarity ratio, the precision ratio, and the processing time.

The process of making an informed decision on which Internet of Things (IoT) platform to choose is an extremely important one in the modern world. The choice procedure is made more difficult as a result of (a) the vast number of IoT platforms that are offered on the market for IoT applications and (b) the wide diversity of functions and solutions that are provided by these platforms. In this article, the multi-criteria decision-making (MCDM) methodologies for selecting the specific Internet of Things platform are taken into consideration. The TOPSIS method is used in this paper to select the best IoT platform. TOPSIS method is a common MCDM method. TOPSIS method used the idea of the best and cost criteria to compute the distance from it. During the IoT platform choice procedures, relevant aspects, such as the stability, consistency, protection, and privacy of IoT platforms, are regarded to be the most significant ones for making decisions.

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The Internet of Things (IoT) healthcare industry is under tremendous pressure to simplify its secure data communication processes. Patients are beginning to consider healthcare services, such as those relating to wellness promotion, illness prevention, diagnosis, care, and recovery, as ongoing cycles. With the prevalence of chronic illnesses on the rise and public perceptions of healthcare shifting, many people increasingly see modern health services as ongoing commitments. Using data provided through the most cutting-edge technology, efficient healthcare systems should reliably provide all their patients with access to the high-quality, comprehensive medical treatment they can afford. So, this study presents a neutrosophic multicriteria decision-making (MCDM) model to optimize the selection of blockchain communication platforms in IoT healthcare applications. To identify the best blockchain platform for use in healthcare, the Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) technique was created. The proposed model improves the efficiency, accuracy, and reliability for better Blockchain secure communication in the IoT healthcare industry.