Intelligent Systems with Applications最新文献

{"title":"Interpretable machine learning approach to predict Hepatitis C virus NS5B inhibitor activity using voting-based LightGBM and SHAP","authors":"Teuku Rizky Noviandy ,&nbsp;Aga Maulana ,&nbsp;Irvanizam Irvanizam ,&nbsp;Ghazi Mauer Idroes ,&nbsp;Nur Balqis Maulydia ,&nbsp;Trina Ekawati Tallei ,&nbsp;Muhammad Subianto ,&nbsp;Rinaldi Idroes","doi":"10.1016/j.iswa.2025.200481","DOIUrl":"10.1016/j.iswa.2025.200481","url":null,"abstract":"<div><div>Hepatitis C is a pressing global health issue that urgently requires the development of effective antiviral medications. In this study, we focus on targeting the Hepatitis C virus non-structural protein 5B (NS5B) polymerase, a key enzyme in viral RNA replication, to hinder the viral life cycle and reduce viral load. We introduce a computational approach that combines multiple LightGBM models to predict the bioactivity of Hepatitis C virus NS5B inhibitors with enhanced performance. By leveraging a voting mechanism, we achieve a higher predictive performance that surpasses individual LightGBM models. Our model achieves an R-squared (R²) value of 0.760, indicating strong predictive capability, along with a root mean squared error (RMSE) of 0.637, a mean absolute error (MAE) of 0.456, and a Pearson correlation coefficient (PCC) of 0.872, demonstrating the model's precision in predicting inhibitor potency and its strong linear correlation with experimental values. To enhance the interpretability of the model, we performed SHAP analysis, which identified critical molecular features influencing bioactivity and facilitated a deeper understanding of the model's decision-making process. Validation through Y-Scrambling tests confirmed that our model's accuracy significantly exceeds what would be expected by random chance alone, ensuring its robustness and reliability. This study demonstrates the power of ensemble machine learning in computational chemistry and drug design, offering a methodologically transparent and interpretable framework for predicting compound potency, which is critical for virtual screening in early-stage drug development. Our approach not only accelerates the discovery of potent NS5B inhibitors but also provides insights into the molecular determinants of bioactivity, underscoring the potential of machine learning in advancing antiviral research. Future research should focus on integrating QSAR modeling with other computational methods and experimental validation to fully realize the potential of this approach in discovering novel HCV therapeutics.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200481"},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drug repositioning framework using embedding drug-protein-disease similarities with graph convolution network and ensemble learning","authors":"Hanaa Torkey ,&nbsp;Heba El-Behery ,&nbsp;Abdel-Fattah Atti ,&nbsp;Nawal El-Fishawy","doi":"10.1016/j.iswa.2025.200480","DOIUrl":"10.1016/j.iswa.2025.200480","url":null,"abstract":"<div><div>The benefits of drug repositioning to the pharmaceutical industry have garnered significant attention in the field of drug development in recent years. Deep learning techniques have significantly improved drug repositioning by studying therapeutic drug profiles, diseases, and proteins. As the number of drugs increases, their targets and interactions generate imbalanced data, which may be undesirable as input to computational prediction model. The approach proposed in this paper uses a hierarchical network embedding technique and a graph autoencoder (GAE) scheme to solve this problem. The approach extracts embedding feature vectors of drugs and targets from a heterogeneous multi-source network to predict unknown drug-target interactions (DTIs). We employ a Meta-Path instance that has extensive drug and target characteristic data. The effectiveness of utilizing Meta-Path instance, the number of attention heads, and Graph Convolutional Network (GCN) and ensemble learning algorithm is analyzed on gold-standard datasets to evaluate the accuracy of the model and validity of the discovered DTI. The results achieved by our model using 10-fold cross-validation testing showed an improvement of 2.52 % in prediction accuracy, 4.2 % in recall, 3.94 % in AUC, and 3.6 % in F-score compared to state-of-the-art methods.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200480"},"PeriodicalIF":0.0,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel improved horned lizard optimization algorithm to identify optimal parameters of adaptive fuzzy logic MPPT for performance boosting of PEM fuel cell","authors":"Hegazy Rezk ,&nbsp;Anas Bouaouda ,&nbsp;Fatma A. Hashim","doi":"10.1016/j.iswa.2025.200478","DOIUrl":"10.1016/j.iswa.2025.200478","url":null,"abstract":"<div><div>Horned Lizard Optimization Algorithm (HLOA) is a newly developed swarm-based metaheuristic technique that emulates the defensive behaviors of the horned lizard in nature. Like other algorithms, HLOA has certain limitations, including the tendency to become trapped in local optima due to a rapid loss of population diversity during the optimization process. This often results in premature convergence, particularly in complex optimization problems. To address these issues, this paper introduces an improved version of HLOA, named iHLOA, which incorporates two distinct strategies. First, the strengthened convergence strategy is utilized to improve the quality of individuals and accelerate the algorithmʼs convergence. Second, the mutation strategy is integrated to significantly boost population diversity, enhancing HLOAʼs ability to escape local minima. Various validation tests conducted on the CEC-2022 benchmark test demonstrate the effectiveness of the iHLOA algorithm in tackling global optimization challenges. Additionally, iHLOA was applied to determine the optimal gains for adaptive Fuzzy Logic Control (FLC) based MPPT to maximize energy harvested from the Proton Exchange Membrane Fuel Cell (PEMFC). The results demonstrate iHLOAʼs superiority over other algorithms, including the Seagull Optimization Algorithm (SOA), Black Widow Optimization Algorithm (BWOA), Sinh Cosh Optimizer (SCHO), Osprey Optimization Algorithm (OOA), Whale Optimization Algorithm (WOA), Greylag Goose Optimization (GGO), and the standard HLOA. iHLOA achieved the best performance with a value of 1.7755, followed by SCHO with 1.7806, while GGO recorded the worst performance at 1.8494. Additionally, iHLOA demonstrated superior stability with the lowest standard deviation (STD) of 0.0122, followed by SOA with 0.0193, while GGO had the highest STD of 0.1101. Furthermore, compared with the classical FLC-MPPT, the proposed FLC-MPPT based on iHLOA achieves faster tracking speeds and reduces oscillations around the MPP in a steady state.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200478"},"PeriodicalIF":0.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid recommendation system using association rule mining, i-ALS algorithm, and SVD++ approach: A case study of a B2B company","authors":"Thamer Saraei,&nbsp;Maha Benali,&nbsp;Jean-Marc Frayret","doi":"10.1016/j.iswa.2025.200477","DOIUrl":"10.1016/j.iswa.2025.200477","url":null,"abstract":"<div><div>In the field of recommendation systems, collaborative filtering is a widely used technique. It provides recommendations to active users based on the ratings provided by similar users. However, this method may reduce the accuracy of user preference predictions and lead to lower-quality recommendations in cases of high data sparsity. This issue is often observed in the Business-to-Business (B2B) context, where user-generated reviews are often sparse. To overcome this challenge, we present a novel hybrid approach that explores product taxonomies and association rule mining combined with an advanced method for initialization. Our approach first involves generating a new explicit taxonomy based solely on textual product descriptions and extending the user–product matrix using association rule mining results. Second, complementary items are added to the user–item matrix based on users’ purchasing behaviors, as emphasized by the extracted association rules. Finally, we use the implicit Alternating Least Squares (i-ALS) algorithm and initialize the latent factor matrices with values obtained through the singular value decomposition approach (BLS-SVD++). This hybrid approach is tested and compared with conventional approaches, considering a real-world case study of a distributor located in Quebec. The results obtained from feedback implicitly inferred from sales data demonstrated improved RS performance compared to conventional approaches.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200477"},"PeriodicalIF":0.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TourismNER: A Tourism Named Entity Recognition method based on entity boundary joint prediction","authors":"Kai Gao ,&nbsp;Jiahao Zhou ,&nbsp;Yunxian Chi ,&nbsp;Yimin Wen","doi":"10.1016/j.iswa.2025.200475","DOIUrl":"10.1016/j.iswa.2025.200475","url":null,"abstract":"<div><div>Tourism named entity recognition is indispensable in tourism information extraction, and plays a crucial role in constructing tourism knowledge map and enhancing tourism knowledge quiz system. The difficulty of tourism named entity recognition lies in its complex nested structure, and the lengthy entity naming length. To address these existing problems, we propose a tourism named entity recognition model that jointly predicts entity boundaries, adopting a training strategy of data preprocessing to enhance the model’s ability for tourism named entity boundary recognition, while our model introduces a pre-trained Bert model as well as BiLSTM coding to enhance the representation of the model’s contexts, and uses a combined predictor of Biaffine and MLP to enhance the model’s recognition performance for boundaries, as well as introducing label smoothing cross entropy to smooth the target labels during the training process. Experiments are conducted on three datasets with different granularities. From the analysis of the experimental results, it can be seen that the named entity recognition method achieves higher accuracy and F1 value compared with the optimal baseline model, and also proves the effectiveness and generality of the modeling method proposed in this paper.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200475"},"PeriodicalIF":0.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing sparse data challenges in recommendation systems: A systematic review of rating estimation using sparse rating data and profile enrichment techniques","authors":"Thennakoon Mudiyanselage Anupama Udayangani Gunathilaka,&nbsp;Prabhashrini Dhanushika Manage,&nbsp;Jinglan Zhang,&nbsp;Yuefeng Li,&nbsp;Wayne Kelly","doi":"10.1016/j.iswa.2024.200474","DOIUrl":"10.1016/j.iswa.2024.200474","url":null,"abstract":"<div><div>E-commerce recommendation systems enhance the user experience by providing customized suggestions tailored to user preferences. They analyze user interactions, such as ratings, to identify user preferences and recommend relevant items accordingly. The sparsity of user–item rating data poses a significant obstacle for Recommender Systems, making it difficult to model user preferences effectively. This issue is particularly evident in collaborative filtering techniques, where the accuracy of user/item similarity calculations and latent factor identification are compromised. Therefore, the sparsity adversely affects the accuracy, coverage, scalability, and transparency of the recommendations, posing significant challenges. Various approaches have been developed to estimate sparse values from the available ratings and improved user/item profiles with side information or sparse ratings to improve the estimation by addressing the four challenges. Despite extensive research in this area, there is a lack of comprehensive surveys that specifically explore estimation methods using sparse rating data and profile enrichment techniques focusing on overcoming challenges that occur due to sparsity such as reduced coverage, transparency, scalability, and accuracy. Understanding the effectiveness of these approaches, their impact on recommendations and future research directions is a crucial area of the literature. This study seeks to examine the individual effects of rating-based estimation and profile enrichment-based estimation methods in Collaborative Filtering recommender systems, to address challenges related to sparsity, identify research gaps, and suggest future research directions. It also provides readers with information on methodologies, available datasets with varying levels of sparsity, and ongoing research challenges in this field.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200474"},"PeriodicalIF":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An appraisal of backscatter removal and refraction calibration models for improving the performance of vision-based mapping and navigation in shallow underwater environments","authors":"Fickrie Muhammad ,&nbsp;Poerbandono ,&nbsp;Harald Sternberg ,&nbsp;Eka Djunarsjah ,&nbsp;Hasanuddin Z Abidin","doi":"10.1016/j.iswa.2025.200476","DOIUrl":"10.1016/j.iswa.2025.200476","url":null,"abstract":"<div><div>Vision-based mapping (VbM) is one of the fundamental origins of automation in remote and autonomous spatial data acquisitions. Complexity in obtaining accurate data arises when such a method is applied in the underwater environment. Non-uniform illumination and refraction distortion are the most common problems encountered in underwater VbM. This study addresses this by employing backscatter removal to enhance image clarity and a pinhole-axial (Pinax) camera model to adjust the refraction distortion. In particular, the methods are computed in the robot operating system (ROS), publishing the enhanced images as separated image nodes in real-time and enabling seamless integration to the VbM pipeline. It is argued that the proposed VbM-dedicated models can significantly improve the feature detection method and conformity of object positions underwater around the camera's motion. Simulation datasets are generated to evaluate the sensitivity to varying turbidity levels to test the method's sensitivity. Additionally, field experiments with GoPro 10 hardware in Pramuka Island Waters, Indonesia, offer real-world context for the study's relevance to distinct underwater circumstances. Furthermore, additional visual-inertial datasets quantify the overall performance, especially in retrieving metric positioning information. The research shows efficient backscatter removal improves feature detection robustness, especially in murky water conditions. Refraction correction eliminates the bowing effect from missing ground control points in underwater environments. The study is significant because it emphasizes how vital image enhancement and refraction calibration are to obtaining &lt;4 % trajectory error of VbM. Overall, the proposed VbM pipeline can maintain &lt;5 cm trajectory error compared to the standard VbM pipeline. The results highlight the need for a comprehensive strategy to advance underwater mapping and navigation technology to deliver accurate and dependable outcomes in various underwater situations.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200476"},"PeriodicalIF":0.0,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust vector-weighted and matrix-weighted multi-view hard c-means clustering","authors":"Zhe Liu ,&nbsp;Sarah Aljohani ,&nbsp;Sijia Zhu ,&nbsp;Tapan Senapati ,&nbsp;Gözde Ulutagay ,&nbsp;Salma Haque ,&nbsp;Nabil Mlaiki","doi":"10.1016/j.iswa.2024.200470","DOIUrl":"10.1016/j.iswa.2024.200470","url":null,"abstract":"<div><div>With the rapid advancement of information technology, multi-view data has become ubiquitous, prompting extensive attention towards multi-view clustering algorithms. Despite significant strides, several challenges persist: (1) the prevalence of noise and outliers in real-world multi-view data often compromises the efficacy of clustering; (2) most existing multi-view clustering algorithms predominantly assess the overall contribution of each view, while neglecting the intra-view contributions. In this paper, we first propose a robust vector-weighted multi-view hard <span><math><mi>c</mi></math></span>-means (VW-MVHCM) clustering, drawing inspiration from the single-view alternative hard <span><math><mi>c</mi></math></span>-means. A distinctive feature of VW-MVHCM is the substitution of the conventional Euclidean norm with a non-Euclidean norm metric, enhancing its resilience to noise and outliers. Additionally, we introduce view weights to learn the contribution of each view in clustering. On this basis, we further propose a robust matrix-weighted multi-view hard <span><math><mi>c</mi></math></span>-means (MW-MVHCM) clustering, which assigns view-specific weights at the cluster level, allowing for more detailed intra-view contribution modeling. This matrix-weighted approach enables MW-MVHCM to dynamically capture the varying importance of each view across clusters, improving clustering performance. We design an optimization scheme to obtain the optimal results of VW-MVHCM and MW-MVHCM. Experimental results on benchmark datasets demonstrate that our proposed algorithms outperform existing multi-view clustering algorithms, showcasing their robustness and effectiveness in real-world scenarios.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200470"},"PeriodicalIF":0.0,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Virtualization resource scheduling and optimization method based on swarm intelligent systems","authors":"Jun Zhao","doi":"10.1016/j.iswa.2024.200469","DOIUrl":"10.1016/j.iswa.2024.200469","url":null,"abstract":"<div><div>Efficient scheduling of virtualized resources can not only meet the service needs of users, but also achieve the optimal allocation of resources and the stable operation of the system. However, due to the dynamic and diversity of virtualized resources, the traditional scheduling methods have been difficult to meet the actual needs. Therefore, a virtual resource scheduling and optimization method based on Swarm Intelligence System (SIS) is proposed in this paper. The core idea of this method is to transform the Virtualized Resource Scheduling (VRS) problem into a multi-objective optimization problem, and use the particle swarm optimization algorithm of SIS to search for the optimal solution. By updating the speed and position of the particles, the scheduling scheme is optimized iteratively to maximize the utilization of resources and optimize the performance of the system. The experimental results show that the SIS-based virtual resource scheduling method can significantly improve the resource utilization and system performance while meeting the needs of users. Compared with other scheduling methods, this method has better adaptability and robustness, and provides a new solution for virtualization resource scheduling in cloud computing environment.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200469"},"PeriodicalIF":0.0,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid machine learning framework by incorporating categorical boosting and manifold learning for financial analysis","authors":"Yuyang Zhao ,&nbsp;Hongbo Zhao","doi":"10.1016/j.iswa.2024.200473","DOIUrl":"10.1016/j.iswa.2024.200473","url":null,"abstract":"<div><div>The financial analysis is essential to evaluate and assess the financial behavior and risk during the financial activities. However, it is challenging to implement the financial analysis due to the complexity of financial features and their interaction mechanism. This study developed a hybrid machine-learning framework incorporating categorical boosting (CatBoost) and manifold learning for financial analysis. CatBoost was employed to capture the financial mechanism and characterize the complex and nonlinear relationship between the financial feature and the associated financial behavior. Manifold learning was utilized to select and extract the critical financial features. The developed framework was verified and illustrated by the synthetic datasets, which are based on the financial model for the loan evaluation. The overall accuracy of the CatBoost model increased from 81.5 % to 99.1 %, and the accuracy for predicting unapproved loans increased from 64 % to 98.88 %. The developed framework significantly improves the prediction accuracy of loan-approved status and characterizes the financial behavior and mechanism well. The developed hybrid framework distinguishes between various financial features and the associated loan-approved status. Based on the developed framework, it also found that credit score and annual income are the two essential features, and the contribution of other features is almost negligible. The developed framework revealed that a credit score of 500 and an annual income of 70,000 are critical thresholds for loan approval, as set by the financial analysis model used to generate the dataset. The results show that the developed framework could extract the financial features and capture the financial mechanism during the financial analysis. It provides a scientific, reasonable, and promising approach to financial analysis and understanding financial behavior.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"25 ","pages":"Article 200473"},"PeriodicalIF":0.0,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}