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Rafael Falcon

Bio: Rafael Falcon is an academic researcher from University of Ottawa. The author has contributed to research in topics: Wireless sensor network & Rough set. The author has an hindex of 18, co-authored 90 publications receiving 1060 citations. Previous affiliations of Rafael Falcon include Hanyang University & Central University of Las Villas.


Papers
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Journal ArticleDOI
TL;DR: A goal-driven overview of numerous theoretical developments recently reported in this area, and an overview of the existing software tools enabling the implementation of both existing FCM schemes as well as prospective theoretical and/or practical contributions.
Abstract: Fuzzy cognitive maps (FCMs) keep growing in popularity within the scientific community. However, despite substantial advances in the theory and applications of FCMs, there is a lack of an up-to-date, comprehensive presentation of the state-of-the-art in this domain. In this review study we are filling that gap. First, we present basic FCM concepts and analyze their static and dynamic properties, and next we elaborate on existing algorithms used for learning the FCM structure. Second, we provide a goal-driven overview of numerous theoretical developments recently reported in this area. Moreover, we consider the application of FCMs to time series forecasting and classification. Finally, in order to support the readers in their own research, we provide an overview of the existing software tools enabling the implementation of both existing FCM schemes as well as prospective theoretical and/or practical contributions.

170 citations

Proceedings ArticleDOI
01 Dec 2016
TL;DR: This paper addresses the detection of malicious URLs as a binary classification problem and study the performance of several well-known classifiers, namely Naïve Bayes, Support Vector Machines, Multi-Layer Perceptron, Decision Trees, Random Forest and k-Nearest Neighbors.
Abstract: The World Wide Web supports a wide range of criminal activities such as spam-advertised e-commerce, financial fraud and malware dissemination. Although the precise motivations behind these schemes may differ, the common denominator lies in the fact that unsuspecting users visit their sites. These visits can be driven by email, web search results or links from other web pages. In all cases, however, the user is required to take some action, such as clicking on a desired Uniform Resource Locator (URL). In order to identify these malicious sites, the web security community has developed blacklisting services. These blacklists are in turn constructed by an array of techniques including manual reporting, honeypots, and web crawlers combined with site analysis heuristics. Inevitably, many malicious sites are not blacklisted either because they are too recent or were never or incorrectly evaluated. In this paper, we address the detection of malicious URLs as a binary classification problem and study the performance of several well-known classifiers, namely Naive Bayes, Support Vector Machines, Multi-Layer Perceptron, Decision Trees, Random Forest and k-Nearest Neighbors. Furthermore, we adopted a public dataset comprising 2.4 million URLs (instances) and 3.2 million features. The numerical simulations have shown that most classification methods achieve acceptable prediction rates without requiring either advanced feature selection techniques or the involvement of a domain expert. In particular, Random Forest and Multi-Layer Perceptron attain the highest accuracy.

78 citations

Journal ArticleDOI
TL;DR: This article elaborate on WSRNs from two unique standpoints: robot task allocation and robot task fulfillment, which enables robots to fulfill the assigned tasks through intelligent mobility scheduling.
Abstract: Wireless sensor and robot networks (WSRNs) have emerged as a paradigmatic class of cyberphysical systems with cooperating objects. Due to the robots' potential to unleash a wider set of networking means and thus augment the network performance, WSRNs have rapidly become a hot research area. In this article, we elaborate on WSRNs from two unique standpoints: robot task allocation and robot task fulfillment. The former deals with robots cooperatively deciding on the set of tasks to be individually carried out to achieve a desired goal; the latter enables robots to fulfill the assigned tasks through intelligent mobility scheduling.

70 citations

Journal ArticleDOI
TL;DR: This paper reviews the application of several methodologies under the CI umbrella to the WSAN field and describes and categorizes existing works leaning on fuzzy systems, neural networks, evolutionary computation, swarm intelligence, learning systems, and their hybridizations to well-known or emerging WSAN problems along five major axes.
Abstract: Wireless sensor and actuator networks (WSANs) are heterogeneous networks composed of many different nodes that can cooperatively sense the environment, determine an appropriate action to take, then change the environment’s state after acting on it. As a natural extension of wireless sensor networks (WSNs), WSANs inherit from them a variety of research challenges and bring forth many new ones. These challenges are related to dealing with imprecise and vague information, solving complicated optimization problems or collecting and processing data from multiple sources. Computational intelligence (CI) is an overarching term denoting a conglomerate of biologically and linguistically inspired techniques that provide robust solutions to NP-hard problems, reason in imprecise terms and yield high-quality yet computationally tractable approximate solutions to real-world problems. Many researchers have consequently turned to CI in hope of finding answers to a plethora of WSAN-related challenges. This paper reviews the application of several methodologies under the CI umbrella to the WSAN field. We describe and categorize existing works leaning on fuzzy systems , neural networks , evolutionary computation , swarm intelligence , learning systems , and their hybridizations to well-known or emerging WSAN problems along five major axes: 1) actuation; 2) communication; 3) sink mobility; 4) topology control; and 5) localization. The survey offers informative discussions to help reason through all the studies under consideration. Finally, we point to future research avenues by: 1) suggesting suitable CI techniques to specific problems; 2) borrowing concepts from WSNs that have yet to be applied to WSANs; or 3) describing the shortcomings of current methods in order to spark interest on the development of more refined models.

54 citations

01 Jan 2011
TL;DR: In this paper, a hybrid meta-heuristic approach between genetic algorithms and ant colony optimization is proposed to solve the one-commodity traveling salesman problem with selective pickup an delivery.
Abstract: This paper discusses a novel combinatorial optimization problem which arises in the domain of wireless sensor networks. A mobile robot with limited cargo capacity replaces damaged sensors, previously deployed over an area of interest, with passive ones so as to preserve the network coverage. The one-commodity traveling salesman problem with selective pickup an delivery is strongly related to the pickup and delivery traveling salesman problem and the prize-collecting travelling salesman problem. The problem is characterized by the fact that the demand of any delivery customer can be met by a relatively large number of pickup customers. While all delivery spots are to be visited, only profitable pickup locations will be included in the tour. A hybrid meta-heuristic approach between genetic algorithms and ant colony optimization is put forward.

52 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
01 Nov 2018-Heliyon
TL;DR: The study found that neural-network models such as feedforward and feedback propagation artificial neural networks are performing better in its application to human problems and proposed feedforwardand feedback propagation ANN models for research focus based on data analysis factors like accuracy, processing speed, latency, fault tolerance, volume, scalability, convergence, and performance.

1,471 citations