Saad M. Darwish^1,*, Abdul Rahman M. Sabri², Dhafar Hamed Abd², Adel A. Elzoghabi¹

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1595-1624, 2024, DOI:10.32604/csse.2024.054615 - 22 November 2024

Abstract The number of blogs and other forms of opinionated online content has increased dramatically in recent years. Many fields, including academia and national security, place an emphasis on automated political article orientation detection. Political articles (especially in the Arab world) are different from other articles due to their subjectivity, in which the author’s beliefs and political affiliation might have a significant influence on a political article. With categories representing the main political ideologies, this problem may be thought of as a subset of the text categorization (classification). In general, the performance of machine learning models… More >

Menghan Zhang^1,2, Fangfang Shan^1,2,*, Mengyao Liu^1,2, Zhenyu Wang^1,2

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1565-1580, 2024, DOI:10.32604/cmc.2024.056008 - 15 October 2024

Abstract With the emergence and development of social networks, people can stay in touch with friends, family, and colleagues more quickly and conveniently, regardless of their location. This ubiquitous digital internet environment has also led to large-scale disclosure of personal privacy. Due to the complexity and subtlety of sensitive information, traditional sensitive information identification technologies cannot thoroughly address the characteristics of each piece of data, thus weakening the deep connections between text and images. In this context, this paper adopts the CLIP model as a modality discriminator. By using comparative learning between sensitive image descriptions and… More >

Yali Si^1,2, Feng Li^1,*, Shan Zhong^1,2, Chenghang Huo³, Jing Chen⁴, Jinglian Liu^1,2

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 685-706, 2024, DOI:10.32604/cmc.2024.055141 - 15 October 2024

Abstract Point-of-interest (POI) recommendations in location-based social networks (LBSNs) have developed rapidly by incorporating feature information and deep learning methods. However, most studies have failed to accurately reflect different users’ preferences, in particular, the short-term preferences of inactive users. To better learn user preferences, in this study, we propose a long-short-term-preference-based adaptive successive POI recommendation (LSTP-ASR) method by combining trajectory sequence processing, long short-term preference learning, and spatiotemporal context. First, the check-in trajectory sequences are adaptively divided into recent and historical sequences according to a dynamic time window. Subsequently, an adaptive filling strategy is used to… More >

Amira M. Idrees^1,*, Abdul Lateef Marzouq Al-Solami²

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1115-1133, 2024, DOI:10.32604/cmc.2023.046457 - 30 January 2024

Abstract The developed system for eye and face detection using Convolutional Neural Networks (CNN) models, followed by eye classification and voice-based assistance, has shown promising potential in enhancing accessibility for individuals with visual impairments. The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system. This research significantly contributes to the field of accessibility technology by integrating computer vision, natural language processing, and voice technologies. By leveraging these advancements, the developed system offers a practical and efficient solution for assisting blind individuals. The modular… More >

Adnan Ali¹, Jinlong Li¹, Huanhuan Chen¹, Uzair Aslam Bhatti², Asad Khan^3,*

Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 241-258, 2023, DOI:10.32604/iasc.2023.041645 - 27 February 2024

Abstract Spammer detection is to identify and block malicious activities performing users. Such users should be identified and terminated from social media to keep the social media process organic and to maintain the integrity of online social spaces. Previous research aimed to find spammers based on hybrid approaches of graph mining, posted content, and metadata, using small and manually labeled datasets. However, such hybrid approaches are unscalable, not robust, particular dataset dependent, and require numerous parameters, complex graphs, and natural language processing (NLP) resources to make decisions, which makes spammer detection impractical for real-time detection. For… More >

Wenlong Zhu^1,2,*, Yu Miao¹, Shuangshuang Yang³, Zuozheng Lian^1,2, Lianhe Cui¹

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3095-3117, 2023, DOI:10.32604/cmc.2023.045646 - 26 December 2023

Abstract Influence Maximization (IM) aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes. However, most existing studies on the IM problem focus on static social network features, while neglecting the features of temporal social networks. To bridge this gap, we focus on node features reflected by their historical interaction behavior in temporal social networks, i.e., interaction attributes and self-similarity, and incorporate them into the influence maximization algorithm and information propagation model. Firstly, we propose… More >

Wenlong Zhu^1,2,*, Yang Huang¹, Shuangshuang Yang³, Yu Miao¹, Chongyuan Peng¹

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1977-1994, 2023, DOI:10.32604/cmc.2023.040998 - 30 August 2023

Abstract The influence maximization (IM) problem aims to find a set of seed nodes that maximizes the spread of their influence in a social network. The positive influence maximization (PIM) problem is an extension of the IM problem, which consider the polar relation of nodes in signed social networks so that the positive influence of seeds can be the most widely spread. To solve the PIM problem, this paper proposes the polar and decay related independent cascade (IC-PD) model to simulate the influence propagation of nodes and the decay of information during the influence propagation in… More >

Daniyal Amankeldin¹, Lyailya Kurmangaziyeva², Ayman Mailybayeva², Natalya Glazyrina¹, Ainur Zhumadillayeva^1,*, Nurzhamal Karasheva³

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1091-1108, 2023, DOI:10.32604/csse.2023.039503 - 26 May 2023

Abstract This paper proposes a deep neural network (DNN) approach for detecting fake profiles in social networks. The DNN model is trained on a large dataset of real and fake profiles and is designed to learn complex features and patterns that distinguish between the two types of profiles. In addition, the present research aims to determine the minimum set of profile data required for recognizing fake profiles on Facebook and propose the deep convolutional neural network method for fake accounts detection on social networks, which has been developed using 16 features based on content-based and profile-based… More >

Abdelwahed Motwakel^1,*, Badriyya B. Al-onazi², Jaber S. Alzahrani³, Sana Alazwari⁴, Mahmoud Othman⁵, Abu Sarwar Zamani¹, Ishfaq Yaseen¹, Amgad Atta Abdelmageed¹

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3321-3338, 2023, DOI:10.32604/csse.2023.033901 - 03 April 2023

Abstract Arabic is the world’s first language, categorized by its rich and complicated grammatical formats. Furthermore, the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for verbs and nouns. The Arabic language consists of distinct variations utilized in a community and particular situations. Social media sites are a medium for expressing opinions and social phenomena like racism, hatred, offensive language, and all kinds of verbal violence. Such conduct does not impact particular nations, communities, or groups only, extending beyond such areas into people’s everyday lives. This study introduces an… More >

Wenlong Zhu^1,*, Yu Miao¹, Shuangshuang Yang², Zuozheng Lian¹, Lianhe Cui¹

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3111-3131, 2023, DOI:10.32604/cmc.2023.036159 - 31 March 2023

Abstract Influence maximization of temporal social networks (IMT) is a problem that aims to find the most influential set of nodes in the temporal network so that their information can be the most widely spread. To solve the IMT problem, we propose an influence maximization algorithm based on an improved K-shell method, namely improved K-shell in temporal social networks (KT). The algorithm takes into account the global and local structures of temporal social networks. First, to obtain the kernel value Ks of each node, in the global scope, it layers the network according to the temporal characteristic… More >