Suneel Kumar Rath¹, Madhusmita Sahu¹, Shom Prasad Das², Junali Jasmine Jena³, Chitralekha Jena⁴, Baseem Khan^5,6,7,*, Ahmed Ali⁷, Pitshou Bokoro⁷

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1513-1536, 2024, DOI:10.32604/csse.2024.057067 - 22 November 2024

Abstract Redundancy, correlation, feature irrelevance, and missing samples are just a few problems that make it difficult to analyze software defect data. Additionally, it might be challenging to maintain an even distribution of data relating to both defective and non-defective software. The latter software class’s data are predominately present in the dataset in the majority of experimental situations. The objective of this review study is to demonstrate the effectiveness of combining ensemble learning and feature selection in improving the performance of defect classification. Besides the successful feature selection approach, a novel variant of the ensemble learning… More >

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 >

Qi Wang¹, Chenxin Li^1,*, Chichen Lin², Weijian Fan³, Shuang Feng¹, Yuanzhong Wang⁴

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3351-3369, 2024, DOI:10.32604/cmc.2024.057191 - 18 November 2024

Abstract News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem. Most previous works only extract features and evaluate media from one dimension independently, ignoring the interconnections between different aspects. This paper proposes a novel news media bias and factuality profiling framework assisted by correlated features. This framework models the relationship and interaction between media bias and factuality, utilizing this relationship to assist in the prediction of profiling results. Our approach extracts features independently while aligning and fusing them through recursive convolution and More >

Alaa Thobhani^1,*, Beiji Zou¹, Xiaoyan Kui¹, Amr Abdussalam², Muhammad Asim³, Naveed Ahmed⁴, Mohammed Ali Alshara^4,5

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2873-2894, 2024, DOI:10.32604/cmc.2024.054841 - 18 November 2024

Abstract Image captioning has gained increasing attention in recent years. Visual characteristics found in input images play a crucial role in generating high-quality captions. Prior studies have used visual attention mechanisms to dynamically focus on localized regions of the input image, improving the effectiveness of identifying relevant image regions at each step of caption generation. However, providing image captioning models with the capability of selecting the most relevant visual features from the input image and attending to them can significantly improve the utilization of these features. Consequently, this leads to enhanced captioning network performance. In light… More >

Tanvir Fatima Naik Bukht¹, Abdulwahab Alazeb², Naif Al Mudawi², Bayan Alabdullah³, Khaled Alnowaiser⁴, Ahmad Jalal¹, Hui Liu^5,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2987-3002, 2024, DOI:10.32604/cmc.2024.053547 - 18 November 2024

Abstract In the field of computer vision and pattern recognition, knowledge based on images of human activity has gained popularity as a research topic. Activity recognition is the process of determining human behavior based on an image. We implemented an Extended Kalman filter to create an activity recognition system here. The proposed method applies an HSI color transformation in its initial stages to improve the clarity of the frame of the image. To minimize noise, we use Gaussian filters. Extraction of silhouette using the statistical method. We use Binary Robust Invariant Scalable Keypoints (BRISK) and SIFT More >

Amjad Rehman¹, Muhammad Mujahid¹, Robertas Damasevicius^2,*, Faten S Alamri³, Tanzila Saba¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2375-2397, 2024, DOI:10.32604/cmes.2024.056937 - 31 October 2024

Abstract This study aims to develop a computational pathology approach that can properly detect and distinguish histology nuclei. This is crucial for histopathological image analysis, as it involves segmenting cell nuclei. However, challenges exist, such as determining the boundary region of normal and deformed nuclei and identifying small, irregular nuclei structures. Deep learning approaches are currently dominant in digital pathology for nucleus recognition and classification, but their complex features limit their practical use in clinical settings. The existing studies have limited accuracy, significant processing costs, and a lack of resilience and generalizability across diverse datasets. We… More >

Suzhe Wang^*, Xueying Zhang, Fenglian Li, Zelin Wu

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1177-1196, 2024, DOI:10.32604/cmc.2024.056016 - 15 October 2024

Abstract Early and timely diagnosis of stroke is critical for effective treatment, and the electroencephalogram (EEG) offers a low-cost, non-invasive solution. However, the shortage of high-quality patient EEG data often hampers the accuracy of diagnostic classification methods based on deep learning. To address this issue, our study designed a deep data amplification model named Progressive Conditional Generative Adversarial Network with Efficient Approximating Self Attention (PCGAN-EASA), which incrementally improves the quality of generated EEG features. This network can yield full-scale, fine-grained EEG features from the low-scale, coarse ones. Specially, to overcome the limitations of traditional generative models… More >

Jiaqi Gao¹, Kangfeng Zheng^1,*, Xiujuan Wang², Chunhua Wu¹, Bin Wu²

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 251-270, 2024, DOI:10.32604/cmc.2024.055560 - 15 October 2024

Abstract User identity linkage (UIL) refers to identifying user accounts belonging to the same identity across different social media platforms. Most of the current research is based on text analysis, which fails to fully explore the rich image resources generated by users, and the existing attempts touch on the multimodal domain, but still face the challenge of semantic differences between text and images. Given this, we investigate the UIL task across different social media platforms based on multimodal user-generated contents (UGCs). We innovatively introduce the efficient user identity linkage via aligned multi-modal features and temporal correlation… More >

Xi Li^1,2, Weiwei Zhu², Qian Li^3,*, Changhui Hou^1,*, Yaozong Zhang¹

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 369-391, 2024, DOI:10.32604/cmc.2024.054982 - 15 October 2024

Abstract Automatically detecting learners’ engagement levels helps to develop more effective online teaching and assessment programs, allowing teachers to provide timely feedback and make personalized adjustments based on students’ needs to enhance teaching effectiveness. Traditional approaches mainly rely on single-frame multimodal facial spatial information, neglecting temporal emotional and behavioural features, with accuracy affected by significant pose variations. Additionally, convolutional padding can erode feature maps, affecting feature extraction’s representational capacity. To address these issues, we propose a hybrid neural network architecture, the redistributing facial features and temporal convolutional network (RefEIP). This network consists of three key components:… More >

Muhammad Naeem Ul Hassan^1,2, Zhengtao Yu^1,2,*, Jian Wang^1,2, Ying Li^1,2, Shengxiang Gao^1,2, Shuwan Yang^1,2, Cunli Mao^1,2

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 951-969, 2024, DOI:10.32604/cmc.2024.054673 - 15 October 2024

Abstract Thanks to the strong representation capability of pre-trained language models, supervised machine translation models have achieved outstanding performance. However, the performances of these models drop sharply when the scale of the parallel training corpus is limited. Considering the pre-trained language model has a strong ability for monolingual representation, it is the key challenge for machine translation to construct the in-depth relationship between the source and target language by injecting the lexical and syntactic information into pre-trained language models. To alleviate the dependence on the parallel corpus, we propose a Linguistics Knowledge-Driven Multi-Task (LKMT) approach to… More >