Showing papers in "Computer Science Review in 2021"

TL;DR: The structural principle, the characteristics, and some kinds of classic models of deep learning, such as stacked auto encoder, deep belief network, deep Boltzmann machine, and convolutional neural network are described.

TL;DR: This study conceptually and empirically explores the most representative FEAs and determines the optimal sets of new features and the quality of the various transformed feature spaces in terms of statistical significance and power analysis, and the FEA efficacy interms of classification accuracy and speed.

TL;DR: The results indicate that the integration of big data and IoT technologies creates exciting opportunities for real-world smart environment applications for monitoring, protection, and improvement of natural resources.

TL;DR: Trends and challenges in the field of data analysis in the context of the new Industrial era are highlighted and discussed such as scalability, cybersecurity, and big data.

TL;DR: This review provides a good reference source in guiding the detection of financial fraud for both academic and practical industries with useful information on the most significant data mining techniques used and shows the list of countries that are exposed to financial fraud.

TL;DR: From the experimental analysis, it is clear that the deep learning model improved the accuracy, scalability, reliability, and performance of the cybersecurity applications when applied in realtime.

TL;DR: A methodical literature review intended to intensively analyze and compare existing primary studies on prototyping with Arduino was presented, finding about 130 of such studies, all peer-reviewed and published within the last 15 years, including these years (2015–2020).

TL;DR: A comprehensive survey of multiple applications of SM analysis using robust machine learning algorithms, which are used in SM analysis.

TL;DR: In this paper, the authors present a study to determine the usefulness, scope, and applicability of this alliance of ML techniques for consumer sentiment analysis (CSA) for online reviews in the domain of hospitality and tourism.

TL;DR: A brief review in the field of clustering in wireless sensor networks based on three different categories, such as classical, optimization, and machine learning techniques, including cluster head selection, routing protocols, reliability, security, and unequal clustering.

TL;DR: In this paper, the authors present a comprehensive survey relating FANET and critical points regarding it, ranging from the categorization of FANet, the architecture of FFANET, types of possible communication in FANets, numerous Mobility Models, constraints in FLANETs, characteristics, and design of FLANSET, routing protocol and routing topology.

TL;DR: This literature review identifies a clear shift of artificial intelligence techniques used in the medical domain, with deep learning methods taking precedence over machine learning methods.

TL;DR: In this paper, a systematic review of the e-tourism management system applied to the smart tourism concepts over the last eight years of publication is presented, in which the authors provide a state-of-the-art taxonomy based on smart concepts and reviews works in different fields against that classification.

TL;DR: In this paper, the authors provide a comprehensive review of existing studies on MTSP and highlight the approaches applied to solve the MTSP as well as its application domains, and propose a taxonomy and a classification of recent studies.

TL;DR: In this paper, the authors present the research efforts from researchers in Smart Farming, who apply innovative technology trends in various crops around Europe and provide and analyze the most significant projects in Europe in the area of Smart Farming.

TL;DR: This paper aims to identify and analyse peer reviewed literature that seeks to use Blockchain smart contracts for securing Internet in general and Internet of Things in particular and presents the systematic analysis of the identified literature.

TL;DR: A comprehensive survey on vulnerability analysis of security solutions for Software-defined Cyber–Physical System and recommends amalgamation of Fog Computing as one of the architectural layers for overcoming a number of vulnerabilities is presented.

TL;DR: This work aims at systematically reviewing and analyzing the research landscape about DL approaches applied to different IoT security scenarios and characterized these studies according to three main research questions, namely, the involved security aspects, the used DL network architectures, and the engaged datasets.

TL;DR: A privacy taxonomy is proposed that establishes a relation between different types of data and suitable PPMs for the characteristics of those data types and identifies open challenges and future directions.

TL;DR: The stack of ensemble (SoE) as discussed by the authors is an ensemble classifier that adopts parallel architecture to combine three individual ensemble learners such as random forest, gradient boosting machine, and extreme gradient boosting machines in a homogeneous manner.

TL;DR: The results show that solutions that provide hands-on experience, team skills development, high level of real-life fidelity are often preferred to other options, with simulation-based solutions showing the highest amount of research and development.

TL;DR: The objective, key features, shortcomings, and benefits of different clustering techniques are examined in this survey to provide a deeper insight of the clustering area to the researchers, which can help them in their new journey of research in this domain.

TL;DR: The state of the art of ontology learning is presented and the challenge of evaluating ontologies to make them reliable is highlighted, since it is not a trivial task in this field; it actually represents a research area on its own.

TL;DR: A sophisticated survey of chain-based routing protocols in WSNs that are successors of PEGASIS in terms of MultiHop/Single hop Communication and Multi hop/Multi hop Communication is provided.

TL;DR: Wang et al. as discussed by the authors investigated the number of publications, subject categories, research areas, cooperation network, research hotspots by combining the bibliometric technique and visual analysis, and then, real-world application cases corresponding to research hotspot are offered.

TL;DR: In this article, the authors present an overview of virtualized data centers and consolidation solutions from the literature and present a brief thematic taxonomy and an illustration of some consolidation solutions.

TL;DR: This study reveals that most of the existing authentication schemes require trusted authorities that are opaque in their functioning, certificate revocation requires heavy computation and storage along with a large amount of lookup time, and more work is needed for the development of lightweight and efficient privacy-preserving authentication schemes in VANETs.

TL;DR: It is inferred that Android is more susceptible to security breaches and malware attacks as compared to iOS, which will help make Android safer for users and will further increase its demand as a mobile operating system.

TL;DR: A systematic analysis for selecting the relevant criteria for clustering in VANETs is provided and the relevant directions for future work in this space are suggested.

TL;DR: In this paper, the authors provide a systematic decade review of data-driven models for energy price prediction, focusing on the aspects of the basic model, the data cleaning method, and optimizer.