Abstract Internet and network technologies have evolved dramatically in the last two decades, with rising users' demands to preserve their identities and privacy. Researchers have developed approaches to achieve users' demands, where the biggest part of the internet has formed, the Deep Web. However, as the Deep Web provides the resort for many benign users who desire to preserve their privacy, it... Internet and network technologies have evolved dramatically in the last two decades, with rising users' demands to preserve their identities and privacy. Researchers have developed approaches to achieve users' demands, where the biggest part of the internet has formed, the Deep Web. However, as the Deep Web provides the resort for many benign users who desire to preserve their privacy, it also became the perfect floor for hosting illicit activities, which generated the Dark Web. This leads to the necessity of finding automated solutions to support law and security agencies in collecting information from the Dark Web to disclose such activities. In this paper, we illustrate the concepts needed for the development of a crawler that collects information from a dark website. We start from discussing the three layers of the Internet, the characteristics of the hidden and private networks, and the technical features of Tor network. We also addressed the challenges facing the dark web crawler. Finally, we presented our experimental system that fetches data from a dark market. This approach helps in putting a single dark website under investigation, and can be a seed for future research and development. Read More Read Less

Abstract The objective of personalized information retrieval systems is to infer the real user information need hidden behind his query, and then tailor the search results to this need. The user information need may be relative to one of the user's multiple search contexts, such as user current location, user social context or an important event that occupies the public opinion... The objective of personalized information retrieval systems is to infer the real user information need hidden behind his query, and then tailor the search results to this need. The user information need may be relative to one of the user's multiple search contexts, such as user current location, user social context or an important event that occupies the public opinion of the user community. In this paper we present a new personalized information retrieval approach in which we design and use a user profile. This profile takes into account the different social, situational and temporal contexts of the user previous searches, and we introduced a personal semantics in the representation of these search contexts. The introduced personal semantics is interested by the identification and the valorization of the different cooccurrence relationships between the different relevant components of the user search history. The user profile, boasting its semantic vision, is used to infer the user current information need by the enrichment of the initial user query without going beyond the user current interest. Read More Read Less

Abstract Group decision making is time-consuming, complex, critical and recurring in nature. In group decision making, the decision-makers often perform periodic and repetitive activities. This prompts the decision makers to store their knowledge and their experience invoked in prior problem solving sessions. The development of this group knowledge clearly assists in similar later decision-making activities and reduces the time required to come... Group decision making is time-consuming, complex, critical and recurring in nature. In group decision making, the decision-makers often perform periodic and repetitive activities. This prompts the decision makers to store their knowledge and their experience invoked in prior problem solving sessions. The development of this group knowledge clearly assists in similar later decision-making activities and reduces the time required to come to a decision, particularly, in a critical situation. This shared knowledge constitutes the group memory. In this paper, we develop a Case-Based Reasoning Decision Support System called CBR-DSS that makes use of the rules, policies and standard procedures of prior groups embedded in the group memory. The CBR process is evolved by using the Analytic Hierarchy Process (AHP). In an effort to enhance utility and show the feasibility, we experiment our approach in a bank loan analysis domain, a semi-structured multiple attributes decision-making environment, and compare CBR-DSS with a conventional case based reasoning system. CBR-DSS will compensate for lack of experience of young bank consultants, and allow disseminating and distributing available experience to different bank sites. Read More Read Less

Abstract Video super-resolution techniques are the need of the hour for the high-resolution display devices as the current high-resolution videos are the basic question. Even though there are a large number of techniques employed for the video super-resolution, all these existing techniques face a hectic challenge at various conditions. Thus, this paper proposes an effective video resolution strategy using the hybrid... Video super-resolution techniques are the need of the hour for the high-resolution display devices as the current high-resolution videos are the basic question. Even though there are a large number of techniques employed for the video super-resolution, all these existing techniques face a hectic challenge at various conditions. Thus, this paper proposes an effective video resolution strategy using the hybrid Support vector regression- Actor Critic Neural Network (SVR-ACNN) model for video enhancement. The super-resolution images formed using the individual SVR model and Actor Critic Neural Network are integrated using the weighted average concept. The Actor Critic Neural Network is tuned optimally using the proposed Fractional-based Sine Cosine algorithm (F-SCA) that is responsible for the global optimal convergence. The experimentation of the proposed method utilizes three videos taken from the Cambridgedriving Labeled Video Database (CamVid), and the results are analyzed for three scaling factors. The outcome of the analysis proves that the proposed method offers a better super-resolution image with a better PSNR, SSIM, and SDME of 33.6447dB, 0.9398, and 45.2779, respectively. Read More Read Less