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Shihu Liu, Chunsheng Yang^*, Yingjie Liu
Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 203-223, 2023, DOI:10.32604/iasc.2023.040818
（This article belongs to the Special Issue: Cognitive Granular Computing Methods for Big Data Analysis)
Abstract Many network presentation learning algorithms (NPLA) have originated from the process of the random walk between nodes in recent years. Despite these algorithms can obtain great embedding results, there may be also some limitations. For instance, only the structural information of nodes is considered when these kinds of algorithms are constructed. Aiming at this issue, a label and community information-based network presentation learning algorithm (LC-NPLA) is proposed in this paper. First of all, by using the community information and the label information of nodes, the first-order neighbors of nodes are reconstructed. In the next, the random walk strategy is improved… More >

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Emad Ul Haq Qazi^1,*, Tanveer Zia¹, Muhammad Imran², Muhammad Hamza Faheem¹
Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 225-240, 2023, DOI:10.32604/iasc.2023.041181
Abstract Deep learning is considered one of the most efficient and reliable methods through which the legitimacy of a digital image can be verified. In the current cyber world where deepfakes have shaken the global community, confirming the legitimacy of a digital image is of great importance. With the advancements made in deep learning techniques, now we can efficiently train and develop state-of-the-art digital image forensic models. The most traditional and widely used method by researchers is convolution neural networks (CNN) for verification of image authenticity but it consumes a considerable number of resources and requires a large dataset for training.… More >

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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
（This article belongs to the Special Issue: Deep Learning for Multimedia Processing)
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 example, graph mining requires neighbors’… More >

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Wenbo Zhu¹, Neng Liu¹, Zhengjun Zhu^2,*, Haibing Li¹, Weijie Fu¹, Zhongbo Zhang¹, Xinghao Zhang¹
Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 259-273, 2023, DOI:10.32604/iasc.2023.041860
Abstract The detection of ash content in coal slime flotation tailings using deep learning can be hindered by various factors such as foam, impurities, and changing lighting conditions that disrupt the collection of tailings images. To address this challenge, we present a method for ash content detection in coal slime flotation tailings. This method utilizes chromatographic filter paper sampling and a multi-scale residual network, which we refer to as MRCN. Initially, tailings are sampled using chromatographic filter paper to obtain static tailings images, effectively isolating interference factors at the flotation site. Subsequently, the MRCN, consisting of a multi-scale residual network, is… More >

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Guohui Song^1,2, Yongbin Wang^1,*, Jianfei Li¹, Hongbin Hu¹
Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 275-295, 2023, DOI:10.32604/iasc.2023.041873
（This article belongs to the Special Issue: Applying Computational Intelligence to Social Science Research)
Abstract Recommending high-quality news to users is vital in improving user stickiness and news platforms’ reputation. However, existing news quality evaluation methods, such as clickbait detection and popularity prediction, are challenging to reflect news quality comprehensively and concisely. This paper defines news quality as the ability of news articles to elicit clicks and comments from users, which represents whether the news article can attract widespread attention and discussion. Based on the above definition, this paper first presents a straightforward method to measure news quality based on the comments and clicks of news and defines four news quality indicators. Then, the dataset… More >

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Muhannad J. Alshareef^*
Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 297-313, 2023, DOI:10.32604/iasc.2023.046722
（This article belongs to the Special Issue: Nature-Inspired Algorithms for Engineering Design Optimization)
Abstract Photovoltaic (PV) systems utilize maximum power point tracking (MPPT) controllers to optimize power output amidst varying environmental conditions. However, the presence of multiple peaks resulting from partial shading poses a challenge to the tracking operation. Under partial shade conditions, the global maximum power point (GMPP) may be missed by most traditional maximum power point tracker. The flower pollination algorithm (FPA) and particle swarm optimization (PSO) are two examples of metaheuristic techniques that can be used to solve the issue of failing to track the GMPP. This paper discusses and resolves all issues associated with using the standard FPA method as… More >

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Mostafa Diba^*, Hossein Khosravi
Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 315-332, 2023, DOI:10.32604/iasc.2023.046590
（This article belongs to the Special Issue: Deep Learning, IoT, and Blockchain in Medical Data Processing )
Abstract Convolutional neural networks continually evolve to enhance accuracy in addressing various problems, leading to an increase in computational cost and model size. This paper introduces a novel approach for pruning face recognition models based on convolutional neural networks. The proposed method identifies and removes inefficient filters based on the information volume in feature maps. In each layer, some feature maps lack useful information, and there exists a correlation between certain feature maps. Filters associated with these two types of feature maps impose additional computational costs on the model. By eliminating filters related to these categories of feature maps, the reduction… More >

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