PeerJ Computer Science最新文献

{"title":"International trade market forecasting and decision-making system: multimodal data fusion under meta-learning.","authors":"Yiming Bai, Muhammad Asif","doi":"10.7717/peerj-cs.3120","DOIUrl":"10.7717/peerj-cs.3120","url":null,"abstract":"<p><p>Traditional market analysis tools primarily rely on unidimensional data, such as historical trading records and price trends. However, these data are often insufficient to reflect the actual state of the market fully. This study introduces a meta-learning-based (MLB) multimodal data fusion approach to optimize feature extraction and fusion strategies, addressing the complexity and heterogeneity inherent in international trade market data. Initially, the mel-frequency cepstral coefficients (MFCC) method is employed to transform the original audio signal into more discriminative spectral features. For image data, the convolutional block attention module (CBAM) is incorporated to capture both channel-wise and spatial attention, thereby improving the model's ability to focus on market-relevant information. In the feature fusion stage, a meta-learning bidirectional feature pyramid network (ML-BiFPN) is proposed to refine the interaction of multi-scale information <i>via</i> a bidirectional feature pyramid structure. An adaptive weighting mechanism is employed to adjust the feature fusion ratio dynamically. Experimental results demonstrate that the proposed multimodal data fusion model, ML-BiFPN under meta-learning, significantly outperforms existing methods in prediction performance. When tested on the publicly available Trade Map dataset, the average accuracy improves by 9.37%, and the F1-score increases by 0.0473 compare to multilayer perceptron (MLP), achieving a prediction accuracy of 94.55% and an F1-score of 0.912. Notably, under small sample conditions, the model's advantage becomes even more pronounced, with an average precision (AP) improvement of 2.79%. These findings have significant implications for international trade market forecasting and decision-making, providing enterprises with a more comprehensive understanding of market dynamics, enhancing forecasting accuracy, and supporting scientifically informed decision-making to gain a competitive edge in the marketplace.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3120"},"PeriodicalIF":2.5,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453693/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced text clustering and sentiment analysis framework for online education: a BIF-DCN approach in computer education.","authors":"Qingyun Zhang, Yang Li, Muhammad Sheraz Arshad Malik","doi":"10.7717/peerj-cs.3062","DOIUrl":"10.7717/peerj-cs.3062","url":null,"abstract":"<p><p>Understanding students' emotional responses to course content and assignments is crucial for developing effective teaching strategies and improving online learning resources. To address this need, we propose a novel deep learning-based framework called BERT and BTF-IDF Integrated Framework with Deep Clustering Network (BIF-DCN), designed to accurately analyze student sentiment on educational platforms. The framework combines three key components: Bidirectional Encoder Representations from Transformers (BERT) for initial text feature extraction, Bi-level Term Frequency-Inverse Document Frequency (BTF-IDF) for enhanced feature representation, and an Improved Deep Embedded Clustering (IDEC) model for sentiment classification. BERT captures rich semantic features from student comments, which are further refined using BTF-IDF to highlight informative terms. These features are then clustered using the IDEC model to identify underlying sentiment-based topics. Experimental results show that BIF-DCN achieves higher clustering accuracy than existing IDEC-based and traditional single-model approaches on both public and self-constructed datasets. In addition to performance improvements, our method enables in-depth sentiment analysis of clustered topics, offering practical insights for optimizing teaching materials. This framework provides educators with valuable tools to better understand student needs and deliver more personalized and effective instruction, ultimately enhancing teaching quality and learner satisfaction.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3062"},"PeriodicalIF":2.5,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453793/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Software defined network intrusion system to detect malicious attacks in computer Internet of Things security using deep extractor supervised random forest technique.","authors":"Muhammad Mujahid, Abeer Rashad Mirdad, Faten S Alamri, Anees Ara, Amjad Khan","doi":"10.7717/peerj-cs.3103","DOIUrl":"10.7717/peerj-cs.3103","url":null,"abstract":"<p><p>The architecture of software-defined networking (SDN) involves the separation of the network control plane from the routing plane. If this initiative turns out well, it has the potential to reduce operating expenses and the duration required to provide new services in comparison to traditional networks. However, this architecture has additional security concerns, including a single point of failure that could potentially provide any user with unrestricted access to the entire network. Nevertheless, it is essential to reduce the probability of security breaches. The development of immediate intrusion detection systems (IDSs) that can quickly spot and stop malicious activities like distributed denial of service (DDoS), DoS, web-attacks, and Bot-NET is an important part of SDN architecture. Several researchers are using cutting-edge methods, such as machine learning, to investigate and elucidate the causes behind the sudden rise in attacks and abnormal behavior, but the majority of these methods are deficient in terms of flexibility and accuracy. This study proposed a lightweight method for detecting different SDN attacks from intrusion-defined networks. The lightweight long short-term memory (LSTM) network has the capability to capture temporal patterns and sequential interactions in the SDN data. It also learned important context that is efficient for feature extraction and then developed supervised random forest (SRF) for the attack prediction. The dataset consists of 207,146 rows and 84 features that were preprocessed, including separate features and target attacks. The experiments show that the proposed method achieved 99.93% accuracy for attack detection and 0.0090 loss, confirming its efficacy. We also tested the proposed method on another SDN dataset and achieved 99.43% accuracy for multi-class attack detection. Furthermore, the use of supervised random forest reduces the model's complexity, resulting in increased overall efficiency.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3103"},"PeriodicalIF":2.5,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453748/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multicriteria scheduling of two-subassembly products with batch availability and precedence constraints.","authors":"Zhenxin Wen, Shuguang Li","doi":"10.7717/peerj-cs.3093","DOIUrl":"10.7717/peerj-cs.3093","url":null,"abstract":"<p><p>This article studies the multicriteria problems of scheduling a set of n products on a fabrication facility, focusing on batch availability and precedence constraints. Each product is composed of two distinct subassemblies: a common subassembly, shared across all products, and a unique subassembly unique to each product. The common subassemblies are processed together in batches, with each batch requiring an initial setup, while unique subassemblies are handled individually. The availability of a common subassembly is contingent upon the completion of its entire batch (<i>i.e</i>., batch availability), whereas a unique subassembly becomes available immediately after its processing. The product completion time is determined by the availability of both subassemblies. Strict (weak) precedence means that if a product precedes another, then the latter can start only after the former is completed (the latter cannot start earlier than the former). We propose O(n⁴)-time algorithms to simultaneously optimize makespan and maximum cost, as well as to lexicographically optimize two maximum costs and makespan under strict or weak precedence constraints.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3093"},"PeriodicalIF":2.5,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453827/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BEPCD: an ensemble learning-based intrusion detection framework for in-vehicle CAN bus.","authors":"Bocheng Xu, Fei Cao, Xilong Li, Song Tian, Wenbo Deng, Shudan Yue","doi":"10.7717/peerj-cs.3108","DOIUrl":"10.7717/peerj-cs.3108","url":null,"abstract":"<p><p>With the rapid development and widespread adoption of intelligent vehicles and the Internet of Vehicles (IoV), vehicle security has become a growing concern. Modern vehicles manage key components <i>via</i> the controller area network (CAN) connected electronic control units (ECUs). CAN bus intrusion techniques are the primary methods of compromising the IoV, posing a significant threat to the normal operation of critical vehicle systems, such as the power systems. However, existing attack detection methods still have shortcomings in terms of feature extraction and the diversity of attack type detection. To address these challenges, we propose an intrusion detection framework named basic ensemble and pioneer class decision (BEPCD). The framework first constructs a 15-dimensional feature model to hierarchically characterize CAN bus messages. Subsequently, BEPCD incorporates multi-model ensemble learning enhanced by a Pioneer class selector and confidence-driven voting mechanisms, enabling precise classification of both conventional and emerging attack patterns. Additionally, we analyze the importance of different data features across four machine learning algorithms. Experimental results on public datasets demonstrate that the proposed detection framework effectively detects intrusions in-vehicle CAN bus. Compared to other intrusion detection frameworks, our framework improves the overall F1-score by 1% to 5%. Notably, it achieves an approximately 77.5% performance enhancement in detecting replay attacks.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3108"},"PeriodicalIF":2.5,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453696/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Palmprint recognition based on principal line features.","authors":"Hongxia Wang, Teng Lv","doi":"10.7717/peerj-cs.3109","DOIUrl":"10.7717/peerj-cs.3109","url":null,"abstract":"<p><p>With the increasing prevalence and diversity of imaging devices, palmprint recognition has emerged as a technology that better meets the demands of the modern era. However, traditional manual methods have limitations in effectively extracting palmprint principal line features. To address this, we introduce a novel data augmentation method. First, the wide line extraction (WLE) filter is utilized to specifically target and extract the prominent principal lines of palmprints by leveraging their direction and width characteristics. Then, a Gabor filter is applied to the WLE-extracted results to purify the features and remove fine lines, as fine lines can introduce noise and redundancy that interfere with the accurate extraction of significant principal line features crucial for palmprint recognition. Evaluating this data augmentation across four common Vision Transformer (ViT) classification models, experimental results show that it improves the recognition rates of all databases to varying degrees, with a remarkable 32.9% increase on the high-resolution XINHUA database. With the successful removal of fine lines by WLE, we propose a new Layer Visual Transformer (LViT) design paradigm. For its input, distinct blocking strategies are adopted, carefully designed to partition the data to capture different levels of spatial and feature information, using larger blocks for global structure and smaller ones for local details. The output results of these different blocking strategies are fused by \"sum fusion\" and \"maximum fusion\", and the local and global features are effectively utilized by combining complementary information to improve the recognition performance and get state-of-the-art results on multiple databases. Moreover, LViT requires fewer training iterations due to the synergistic effects of the blocking strategies, optimizing the learning process. Finally, by simulating real-world noise conditions, we comprehensively evaluate LViT and find that, compared with traditional methods, our approach exhibits excellent noise-resistant generalization ability, maintaining stable performance across the PolyU II, IIT Delhi, XINHUA, and NTU-CP-V1 databases.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3109"},"PeriodicalIF":2.5,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453761/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A path aggregation network with deformable convolution for visual object detection.","authors":"Chengming Rao, Zunhao Hu, QiMing Zhao, Min Shan, Li Mao","doi":"10.7717/peerj-cs.3083","DOIUrl":"10.7717/peerj-cs.3083","url":null,"abstract":"<p><p>One of the main challenges encountered in visual object detection is the multi-scale issue. Many approaches have been proposed to tackle this issue. In this article, we propose a novel neck that can perform effective fusion of multi-scale features for a single-stage object detector. This neck, named the deformable convolution and path aggregation network (DePAN), is an integration of a path aggregation network with a deformable convolution block added to the feature fusion branch to improve the flexibility of feature point sampling. The deformable convolution block is implemented by repeated stacking of a deformable convolution cell. The DePAN neck can be plugged in and easily applied to various models for object detection. We apply the proposed neck to the baseline models of Yolov6-N and YOLOV6-T, and test the improved models on COCO2017 and PASCAL VOC2012 datasets, as well as a medical image dataset. The experimental results verify the effectiveness and applicability in real-world object detection.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3083"},"PeriodicalIF":2.5,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453868/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging PSO-MLP for intelligent assessment of student learning in remote environments: a multimodal approach.","authors":"Jing Wang, Muhammad Asif","doi":"10.7717/peerj-cs.3121","DOIUrl":"10.7717/peerj-cs.3121","url":null,"abstract":"<p><p>The rapid advancement of artificial intelligence (AI) has catalyzed transformative changes in education, particularly in mobile and online learning environments. While existing deep learning models struggle to efficiently integrate the complexity of remote education data and optimize model performance, this article proposes an intelligent evaluation method for students' learning states based on multimodal data. First, the joint characteristics of the pre-class mental status survey information and the health big data of teachers and students in the online teaching process constitute input data. Then, the multilayer perceptron (MLP) is used to intelligently identify the students' status and classify their enthusiasm for the class. Finally, the particle swarm optimization (PSO) model is used to optimize the model and improve the overall recognition rate. Compared to traditional methods, the PSO-MLP model with combined multimodal data performs well, achieving an accuracy of 0.891. It provides an operational, technical solution for the education system, provides a new AI foundation for personalized teaching and student health management by accurately assessing students' learning status, and helps to improve the effectiveness and efficiency of remote education.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3121"},"PeriodicalIF":2.5,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453797/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing transformer-based prediction of human microbe-disease associations through integrated loss strategies.","authors":"Rong Zhu, Yong Wang, Junliang Shang, Ling-Yun Dai, Feng Li","doi":"10.7717/peerj-cs.3098","DOIUrl":"10.7717/peerj-cs.3098","url":null,"abstract":"<p><p>Microorganisms play an important role in many complex diseases, influencing their onset, progression, and potential treatment outcomes. Exploring the associations between microbes and human diseases can deepen our understanding of disease mechanisms and assist in improving diagnosis and therapy. However, traditional biological experiments used to uncover such relationships often demand substantial time and resources. In response to these limitations, computational methods have gained traction as more practical tools for predicting microbe-disease associations. Despite their growing use, many of these models still face challenges in terms of accuracy, stability, and adaptability to noisy or sparse data. To overcome the aforementioned limitations, we propose a novel predictive framework, HyperGraph Neural Network with Transformer for Microbe-Disease Associations (HGNNTMDA), designed to infer potential associations between human microbes and diseases. The framework begins by integrating microbe-disease association data with similarity-based features to construct node representations. Two graph construction strategies are employed: a K-nearest neighbor (KNN)-based adjacency matrix to build a standard graph, and a K-means clustering approach that groups similar nodes into clusters, which serve as hyperedges to define the incidence matrix of a hypergraph. Separate hypergraph neural networks (HGNNs) are then applied to microbe and disease graphs to extract structured node-level features. An attention mechanism (AM) is subsequently introduced to emphasize informative signals, followed by a Transformer module to capture contextual dependencies and enhance global feature representation. A fully connected layer then projects these features into a unified space, where association scores between microbes and diseases are computed. For model optimization, we propose a hybrid loss strategy combining contrastive loss and Huber loss. The contrastive loss aids in learning discriminative embeddings, while the Huber loss enhances robustness against outliers and improves predictive stability. The effectiveness of HGNNTMDA is validated on two benchmark datasets-HMDAD and Disbiome-using five-fold cross-validation (5CV). Our model achieves an AUC of 0.9976 on HMDAD and 0.9423 on Disbiome, outperforming six existing state-of-the-art methods. Further case studies confirm its practical value in discovering novel microbe-disease associations.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3098"},"PeriodicalIF":2.5,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transformer-based tokenization for IoT traffic classification across diverse network environments.","authors":"Firdaus Afifi, Faiz Zaki, Hazim Hanif, Nik Aqil, Nor Badrul Anuar","doi":"10.7717/peerj-cs.3126","DOIUrl":"10.7717/peerj-cs.3126","url":null,"abstract":"<p><p>The rapid expansion of the Internet of Things (IoT) has significantly increased the volume and diversity of network traffic, making accurate IoT traffic classification crucial for maintaining network security and efficiency. However, existing traffic classification methods, including traditional machine learning and deep learning approaches, often exhibit critical limitations, such as insufficient generalization across diverse IoT environments, dependency on extensive labelled datasets, and susceptibility to overfitting in dynamic scenarios. While recent transformer-based models show promise in capturing contextual information, they typically rely on standard tokenization, which is ill-suited for the irregular nature of IoT traffic and often remains confined to single-purpose tasks. To address these challenges, this study introduces MIND-IoT, a novel and scalable framework for classifying generalized IoT traffic. MIND-IoT employs a hybrid architecture that combines Transformer-based models for capturing long-range dependencies and convolutional neural networks (CNNs) for efficient local feature extraction. A key innovation is IoT-Tokenize, a custom tokenization pipeline designed to preserve the structural semantics of network flows by converting statistical traffic features into semantically meaningful feature-value pairs. The framework operates in two phases: a pre-training phase utilizing masked language modeling (MLM) on large-scale IoT data (UNSW IoT Traces and MonIoTr) to learn robust representations and a fine-tuning phase that adapts the model to specific classification tasks, including binary IoT <i>vs</i>. non-IoT classification, IoT category classification, and device identification. Comprehensive evaluation across multiple diverse datasets (IoT Sentinel, YourThings, and IoT-FCSIT, in addition to the pre-training datasets) demonstrates MIND-IoT's superior performance, robustness, and adaptability compared to traditional methods. The model achieves an accuracy of up to 98.14% and a 97.85% F1-score, demonstrating its ability to classify new datasets and adapt to emerging tasks with minimal fine-tuning and remarkable efficiency. This research positions MIND-IoT as a highly effective and scalable solution for real-world IoT traffic classification challenges.</p>","PeriodicalId":54224,"journal":{"name":"PeerJ Computer Science","volume":"11 ","pages":"e3126"},"PeriodicalIF":2.5,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12453836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}