Fuzzy granular classifier approach for spam detection

Salehi, Saber; Selamat, Ali; Kuča, Kamil; Krejcar, Ondřej; Sabbah, Thabit

doi:10.3233/jifs-169133

Cited by 9 publications

(2 citation statements)

References 28 publications

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“…The procedure for determining the number of arriving calls N (t), t*0 is a Poisson procedure that has an average rate λ. A modern algorithm technique, namely the "Particle Swarm Optimization" [26][27][28][29], was adopted for channel allocation [30]. This technique depends on a worldwide optimization that depends on "bird flocking" social behavior.…”

Section: Related Workmentioning

confidence: 99%

Sustainable Communication Systems: A Graph-Labeling Approach for Cellular Frequency Allocation in Densely-Populated Areas

2019

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The need for smart and sustainable communication systems has led to the development of mobile communication networks. In turn, the vast functionalities of the global system of mobile communication (GSM) have resulted in a growing number of subscribers. As the number of users increases, the need for efficient and effective planning of the “limited” frequency spectrum of the GSM is inevitable, particularly in densely-populated areas. As such, there are ongoing discussions about frequency (channel) allocation methods to resolve the challenges of channel allocation, which is a complete NP (Nondeterministic Polynomial time) problem. In this paper, we propose an algorithm for channel allocation which takes into account soft constraints (co-channel interference and adjacent channel interference). By using the Manhattan distance concept, this study shows that the formulation of the algorithm is correct and in line with results in the literature. Hence, the Manhattan distance concept may be useful in other scheduling and optimization problems. Furthermore, this unique concept makes it possible to develop a more sustainable telecommunication system with ease of connectivity among users, even when several subscribers are on a common frequency.

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Section: Related Workmentioning

confidence: 99%

Sustainable Communication Systems: A Graph-Labeling Approach for Cellular Frequency Allocation in Densely-Populated Areas

2019

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“…Feature selection is a procedure for finding the minimum number of features from the original one. Besides, determining the minimum number of features is critical for the overall process success and failure [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Email Filtering Using Hybrid Feature Selection Model

Mohammad¹,

Smadi²,

Alwada’n³

2022

Computer Modeling in Engineering &Amp; Sciences

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Undoubtedly, spam is a serious problem, and the number of spam emails is increased rapidly. Besides, the massive number of spam emails prompts the need for spam detection techniques. Several methods and algorithms are used for spam filtering. Also, some emergent spam detection techniques use machine learning methods and feature extraction. Some methods and algorithms have been introduced for spam detecting and filtering. This research proposes two models for spam detection and feature selection. The first model is evaluated with the email spam classification dataset, which is based on reducing the number of keywords to its minimum. The results of this model are promising and highly acceptable. The second proposed model is based on creating features for spam detection as a first stage. Then, the number of features is reduced using three well-known metaheuristic algorithms at the second stage. The algorithms used in the second model are Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO), and these three algorithms are adapted to fit the proposed model. Also, the authors give it the names AABC, AACO, and APSO, respectively. The dataset used for the evaluation of this model is Enron. Finally, well-known criteria are used for the evaluation purposes of this model, such as true positive, false positive, false negative, precision, recall, and F-Measure. The outcomes of the second proposed model are highly significant compared to the first one.

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Ultrasound image assisted diagnosis of hydronephrosis based on CNN neural network

Liu

Wang

2020

Journal of King Saud University - Science

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Fuzzy granular classifier approach for spam detection

Cited by 9 publications

References 28 publications

Sustainable Communication Systems: A Graph-Labeling Approach for Cellular Frequency Allocation in Densely-Populated Areas

Sustainable Communication Systems: A Graph-Labeling Approach for Cellular Frequency Allocation in Densely-Populated Areas

Email Filtering Using Hybrid Feature Selection Model

Ultrasound image assisted diagnosis of hydronephrosis based on CNN neural network

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