Harisu Abdullahi Shehu

Introduction: Fake news poses serious risks to societies by spreading disinformation rapidly and undermining trust in media. Automated methods for detecting fake news therefore need to be explainable to ensure transparency and trustworthiness. However, existing state-of-the-art methods are black-box and computationally expensive, making it difficult for practitioners and policymakers to understand and adopt them in real-world scenarios. Method: Feature selection can significantly enhance model transparency. Evolutionary computation (EC) methods are adaptable and naturally suited for optimization tasks such as feature selection. Among EC methods, Particle Swarm Optimization (PSO) requires few hyperparameters and converges quickly to good solutions. Therefore, this paper proposes an explainable approach to fake news classification using PSO. As mutual information (MI) quantifies the dependency between features and labels, the proposed method incorporates MI in its fitness function to guide the feature selection process and improve performance by eliminating redundant features. Result: Experimental results on three benchmark datasets (BuzzFeed, ISOT, and TruthSeeker) show that the proposed method achieved higher accuracy (85.21% on BuzzFeed, 99.99% on ISOT, and 90.92% on TruthSeeker) than baseline and state-of-the-art methods. Importantly, the proposed method selected a small feature subset, which helps explain model decisions, while requiring significantly less computation time. Discussion: These findings demonstrate the potential of PSO-based explainable approaches for practical and trustworthy fake news detection.

The demand for advanced detection methods and accurate staging remains a global challenge in cancer diagnosis. Even though traditional deep learning models in medical imaging achieve high precision, they suffer from limited explainability and multimodal reasoning due to their black-box nature, thereby limiting their clinical applicability. To address this gap, recent research has increasingly explored multimodal approaches that integrate visual and textual clinical data to enhance diagnostic accuracy and interpretability. This study presents a bibliometric analysis of 408 publications from 2021 to 2025, collected from Web of Science and Scopus, using VOSviewer and R-Bibliometrix to map citation networks, co-authorship, and keyword co-occurrences. The results reveal a rapid growth from 1 publication in 2021 to 269 in 2025, with significant contributions from leading countries and institutions. Thematic analysis indicates a shift from conventional convolutional approaches toward transformer-based and self-supervised methods, alongside increasing attention to multimodal learning in cancer imaging tasks such as breast, lung, and brain cancer analysis. Overall, this study provides a structured overview of the evolving research landscape, highlighting key trends, emerging themes, and research gaps to inform future developments in multimodal artificial intelligence for cancer diagnosis.

This study presents the first dedicated bibliometric analysis of artificial intelligence (AI) and deep learning applications in pediatric radiology and medical imaging, mapping the intellectual structure of a rapidly evolving field. A total of 2688 articles and conference proceedings published between 2005 and 2025 were retrieved from the Web of Science Core Collection and analyzed using Bibliometrix R and VOSviewer. The findings reveal exponential growth in publications, from 7 papers in 2005 to 559 in 2025, with journal articles dominating the corpus (85.9%). The most-cited contributions, led by Kermany et al. (2018) with 2886 citations, are predominantly technical feasibility studies rather than clinical outcome trials, indicating a field that has advanced methodologically but remains in early stages of clinical translation. Thematic mapping identifies convolutional neural networks, pneumonia, and transfer learning as Motor Themes representing methodological maturity in chest imaging, while neuroimaging and image segmentation clusters occupy Niche Themes, reflecting insular development with limited cross-field connectivity. Geographic analysis reveals concentrated co-authorship along US–China and US–Europe corridors, with African, Latin American, and Southeast Asian institutions largely absent from knowledge production networks. Eight of the ten most productive affiliations are North American, highlighting structural inequities that risk producing AI tools optimized for high-resource settings rather than the global pediatric population. This analysis provides an empirical foundation for reorienting the field toward clinical validation, geographic inclusion, and methodological integration across isolated research communities.

Saxitoxin (STX) is one of the most potent marine neurotoxins, produced by several species of freshwater cyanobacteria and marine dinoflagellates. Although omics-based approaches have advanced our understanding of STX biosynthesis in recent decades, the origin, regulation, and ecological drivers of STX in dinoflagellates remain poorly resolved. Specifically, dinoflagellate STX biosynthetic genes (sxt) are extremely fragmented, inconsistently expressed, and unevenly distributed between toxic and non-toxic taxa. Environmental studies further report inconsistent relationships between abiotic factors and STX production, suggesting regulation across multiple genomic, transcriptional, post-transcriptional, and epigenetic levels. These gaps prevent a comprehensive understanding of STX biosynthesis in dinoflagellates and limit the development of accurate predictive models for harmful algal blooms (HABs) and paralytic shellfish poisoning (PSP). Artificial intelligence (AI), including machine learning and deep learning, offers new opportunities in ecological pattern recognition, molecular annotation, and data-driven prediction. This review explores the current state of knowledge and persistent knowledge gaps in dinoflagellate STX research and proposes an AI-integrated multi-omics framework highlighting recommended models for sxt gene identification (e.g., DeepFRI, ProtTrans, ESM-2), evolutionary reconstruction (e.g., PhyloGAN, GNN, PhyloVAE, NeuralNJ), molecular regulation (e.g., MOFA+, LSTM, GRU, DeepMF), and toxin prediction (e.g., XGBoost, LightGBM, LSTM, ConvLSTM). By integrating AI with diverse biological datasets, this novel framework outlines how AI can advance fundamental understanding of STX biosynthesis and inform future applications in HAB monitoring, seafood safety, and PSP risk management in aquaculture and fisheries.

Breast cancer is one of the most prevalent and life-threatening diseases affecting women worldwide. Early and accurate diagnosis is critical for effective treatment and improved patient outcomes. While histopathological image analysis plays a key role in breast cancer diagnosis, the complexity and heterogeneity of these images present significant challenges. To address this, we propose DenseNet-FPA, a novel method that combines Dense Convolutional Networks (DenseNet) with the Flower Pollination Algorithm (FPA) to enhance feature selection and classification in breast cancer histopathology images. DenseNet effectively extracts hierarchical features, while FPA optimizes the selection of the most discriminative features, improving classification accuracy and reducing computational overhead. We evaluated our approach on two publicly available datasets, i.e. BreakHis and BACH, achieving accuracies of 99.32% and 96%, respectively. These results significantly outperform state-of-the-art methods, which achieved <99% on BreakHis and <93% on BACH. Our findings demonstrate significant improvements in binary classification performance, offering a more efficient and accurate approach for breast cancer diagnosis. The proposed method has potential applications in other histopathological analyses and clinical settings.

The agricultural sector faces persistent threats from plant diseases and pests, with Tuta absoluta posing a severe risk to tomato farming by causing up to 100% crop loss. Timely pest detection is essential for effective intervention, yet traditional methods remain labor-intensive and inefficient. Recent advancements in deep learning offer promising solutions, with YOLOv8 emerging as a leading real-time detection model due to its speed and accuracy, outperforming previous models in on-field deployment. This study focuses on the early detection of Tuta absoluta-induced tomato leaf diseases in Sub-Saharan Africa. The first major contribution is the annotation of a dataset (TomatoEbola), which consists of 326 images and 784 annotations collected from three different farms and is now publicly available. The second key contribution is the proposal of a transfer learning-based approach to evaluate YOLOv8’s performance in detecting Tuta absoluta. Experimental results highlight the model’s effectiveness, with a mean average precision of up to 0.737, outperforming other state-of-the-art methods that achieve less than 0.69, demonstrating its capability for real-world deployment. These findings suggest that AI-driven solutions like YOLOv8 could play a pivotal role in reducing agricultural losses and enhancing food security.

The eye socket is a cavity in the skull that encloses the eyeball and its surrounding muscles. It has unique shapes in individuals. This study proposes a new recognition method that relies on the eye socket shape and region. This method involves the utilization of an inverse histogram fusion image to generate Gabor features from the identified eye socket regions. These Gabor features are subsequently transformed into Gabor images and employed for recognition by utilizing both traditional methods and deep‐learning models. Four distinct benchmark datasets (Flickr30, BioID, Masked AT & T, and CK+) were used to evaluate the method's performance. These datasets encompass a range of perspectives, including variations in eye shape, covering, and angles. Experimental results and comparative studies indicate that the proposed method achieved a significantly ( ) higher accuracy (average value greater than 92.18%) than that of the relevant identity recognition method and state‐of‐the‐art deep networks (average value less than 78%). We conclude that this improved generalization has significant implications for advancing the methodologies employed for identity recognition.

Opinion mining has witnessed significant advancements in well-resourced languages. However, for low-resource languages, this landscape remains relatively unexplored. This paper addresses this gap by conducting a comprehensive investigation into sentiment analysis in the context of Hausa, one of the most widely spoken languages within the Afro-Asiatic family. To resolve the problem, three different models based on Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Hierarchical Attention Network (HAN), all tailored to the unique linguistic characteristics of Hausa have been proposed. Additionally, we have developed the first dedicated lexicon dictionary for Hausa sentiment analysis and a customized stemming method to enhance the accuracy of the bag of words approach. Our results indicate that CNN and HAN achieved significantly higher performance compared to other models such as RNN. While the experimental results demonstrate the effectiveness of the developed deep learning models in contrast to the bag of words approach, the proposed stemming method was found to significantly improve the performance of the bag of words approach. The findings from this study not only enrich the sentiment analysis domain for Hausa but also provide a foundation for future research endeavors in similarly underrepresented languages.