Evolving Support Vector Machines using Whale Optimization Algorithm for spam profiles detection on online social networks in different lingual contexts

Al-Zoubi, Ala’ M.; Faris, Hossam; Alqatawna, Ja'far; Hassonah, Mohammad A.

doi:10.1016/j.knosys.2018.04.025

Cited by 127 publications

(29 citation statements)

References 17 publications

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“…By hybridizing an established meta-heuristic technique called Whale Optimization Algorithm (WOA) with Support Vector Machines (SVM), a hybrid machine learning model was proposed with a view to identifying spammers in Twitter. The concept behind using WOA in this hybrid technique was to improve SVM's parameters along with the task of selecting the proper spam recognition features [10].…”

Section: Imentioning

confidence: 99%

Ontology Meter for Twitter Fake Accounts Detection

Jabardi¹,

Hadi²

2021

IJIES

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One of the most popular social media platforms, Twitter is used by millions of people to share information, broadcast tweets, and follow other users. Twitter is an open application programming interface and thus vulnerable to attack from fake accounts, which are primarily created for advertisement and marketing, defamation of an individual, consumer data acquisition, increase fake blog or website traffic, share disinformation, online fraud, and control. Fake accounts are harmful to both users and service providers, and thus recognizing and filtering out such content on social media is essential. This study presents a new approach to detect fake Twitter accounts using ontology and Semantic Web Rule Language (SWRL) rules. SWRL rules-based reasoner is utilized under predefined rules to infer whether the profile is trust or fake. This approach achieves a high detection accuracy of 97%. Furthermore, ontology classifier is an interpretable model that offers straightforward and human-interpretable decision rules.

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Section: Imentioning

confidence: 99%

Ontology Meter for Twitter Fake Accounts Detection

Jabardi¹,

Hadi²

2021

IJIES

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“…The classifier has two parameters called penalty and kernel parameters. These parameters have major effect on SVM performance [21]. The first one plays an important role as a tradeoff between training data error minimization and the maximization of the classification margin.…”

Section: B) Support Vector Machine Classificationmentioning

confidence: 99%

Leaf identification using radial basis function neural networks and SSA based support vector machine

Ahmed

Hussein

2020

PLoS ONE

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In this research, an efficient scheme to identify leaf types is proposed. In that scheme, the leaf boundary points are fitted in a continuous contour using Radial Basis Function Neural Networks (RBFNN) to calculate the centroid of the leaf shape. Afterwards, the distances between predetermined points and the centroid were computed and normalized. In addition, the time complexity of the features' extraction algorithm was calculated. The merit of this scheme is objects' independence to translation, rotation and scaling. Moreover, different classification techniques were evaluated against the leaf shape features. Those techniques included two of the most commonly used classification methods; RBFNN and SVM that were evaluated and compared with other researches that used complex features extraction algorithms with much higher dimensionality. Furthermore, a third classification method with an optimization technique for the SVM using Salp Swarm Algorithm (SSA) was utilized showing a significant improvement over RBFNN and SVM.

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“…Цей метод широко використовують для розпізнавання образів [27] та створення спам-фільтрів [28], в фінансовому прогнозуванні [29], тощо. Методу властива простота реалізації, але застосовується лише для задач з двома класами даних, так для вхідних даних SVM прогнозує одну з двох можливих форм виходу.…”

Section: рис 5 приклад побудови гіперплощини для двохunclassified

Аналіз Основних Моделей Прогнозування Часових Рядів

Андрусенко¹

2020

zhnups

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Метою статті є огляд і класифікація основних моделей прогнозування часових рядів. Розглянуті статистичні моделі експоненційного згладжування, ковзного середнього, регресійні та авторегресійні моделі, їх особливості та сфери використання. Зі структурних методів проведено аналіз моделей на базі нейромереж, ланцюгів Маркова, опорних векторів та дерев рішень. Найкращі результати демонструють комбіновані моделі прогнозування, які розглянуто у сучасних публікаціях в інформаційних системах різноманітного призначення. Подальші дослідження будуть об’єднувати статистичні та структурні моделі для отримання кращого прогнозу.

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Evolving Support Vector Machines using Whale Optimization Algorithm for spam profiles detection on online social networks in different lingual contexts

Cited by 127 publications

References 17 publications

Ontology Meter for Twitter Fake Accounts Detection

Ontology Meter for Twitter Fake Accounts Detection

Leaf identification using radial basis function neural networks and SSA based support vector machine

Аналіз Основних Моделей Прогнозування Часових Рядів

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