Big Data Research最新文献

{"title":"Research on adaptive long-term time series carbon dioxide emission prediction model based on improved multilayer perceptron","authors":"Jiachen Xie ,&nbsp;Jiwei Qin ,&nbsp;Xizhong Qin ,&nbsp;Daishun Cui ,&nbsp;Qiang Li ,&nbsp;Dezhi Sun","doi":"10.1016/j.bdr.2025.100572","DOIUrl":"10.1016/j.bdr.2025.100572","url":null,"abstract":"<div><div>Carbon dioxide (CO₂) emissions play a crucial role in driving global climate change. Precise and reliable predictions of CO₂ emission trends are instrumental in fostering sustainable development and realizing dual-carbon goals. Due to complex human activities, economic development, and meteorological factors, accurate long-term time series prediction of CO₂ emissions encounters numerous challenges, such as the long-term temporal dependencies and complicated non-linear correlation in long-term time series CO₂ emissions. To address these challenges, we propose a long-term time series CO₂ emissions prediction model called CarbonLinear. The proposed CarbonLinear is based on Multilayer Perceptron that can better capture non-linear relationships in long-term time series CO₂ emissions through a flexible network structure and deep connectivity. The proposed CarbonLinear employs an adaptive global-local multiscale integrated modeling architecture to mitigate the data distribution shift problem adaptively. In addition, the proposed CarbonLinear introduces a sequence segmentation module, which allows CarbonLinear to model local features in a long-term time series CO₂ emissions and improves the computational efficiency of the model. Experimental results show that the proposed CarbonLinear performs well on CO₂ emissions datasets from multiple regions, significantly improving over other models. The proposed CarbonLinear provides scientists and policymakers with a more accurate and reliable tool for CO₂ emissions prediction.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"42 ","pages":"Article 100572"},"PeriodicalIF":4.2,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PATH: A discrete-sequence dataset for evaluating online unsupervised anomaly detection approaches for multivariate time series","authors":"Lucas Correia ,&nbsp;Jan-Christoph Goos ,&nbsp;Thomas Bäck ,&nbsp;Anna V. Kononova","doi":"10.1016/j.bdr.2025.100573","DOIUrl":"10.1016/j.bdr.2025.100573","url":null,"abstract":"<div><div>Benchmarking anomaly detection approaches for multivariate time series is a challenging task due to a lack of high-quality datasets. Current publicly available datasets are too small, not diverse and feature trivial anomalies, which hinders measurable progress in this research area. We propose a solution: a diverse, extensive, and non-trivial dataset generated via state-of-the-art simulation tools that reflects realistic behaviour of an automotive powertrain, including its multivariate, dynamic and variable-state properties. Additionally, our dataset represents a discrete-sequence problem, which remains unaddressed by previously-proposed solutions in literature. To cater for both unsupervised and semi-supervised anomaly detection settings, as well as time series generation and forecasting, we make different versions of the dataset available, where training and test subsets are offered in contaminated and clean versions, depending on the task. We also provide baseline results from a selection of approaches based on deterministic and variational autoencoders, as well as a non-parametric approach. As expected, the baseline experimentation shows that the approaches trained on the semi-supervised version of the dataset outperform their unsupervised counterparts, highlighting a need for approaches more robust to contaminated training data. Furthermore, results show that the threshold used can have a large influence on detection performance, hence more work needs to be invested in methods to find a suitable threshold without the need for labelled data.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"42 ","pages":"Article 100573"},"PeriodicalIF":4.2,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effective adaptive res-BiGRU network for pest classification performance based on regionViT-yolov8-aided pest detection technique","authors":"S.M. Mehzabeen,&nbsp;R Gayathri","doi":"10.1016/j.bdr.2025.100571","DOIUrl":"10.1016/j.bdr.2025.100571","url":null,"abstract":"<div><div>Effective pest management often involves the use of appropriate pesticides, and early identification of pests is essential for protecting crops. Timely and accurate identification of pests using deep learning-based approaches have gained traction as effective solutions for addressing agricultural challenges, including the detection of plant diseases and pests. Thus, a pest recognition model is developed to improve the productivity of the crops based on effective deep learning method. This model is implemented by accumulating the required input images, which are then fed into the developed Region Vision Transformer-Yolov8 (RegViT- Yolov8) model for pest detection. Then, the RegionViT features of all Bounded Boxes are extracted from the detected outcome and concatenated together. Further, Principal Component Analysis (PCA) based feature reduction is executed by considering the concatenated features as input. Following feature reduction, pest classification is performed by using the developed Adaptive Residual Bidirectional Gated Recurrent Unit (AR-BiGRU). Moreover, the classification accuracy is enhanced by optimizing the system parameters using an Advanced Random Variable-based Preschool Education Optimization Algorithm (ARV-PEOA). Thus, the effectiveness of this framework is validated with diverse measures and the attained outcome is compared with the existing techniques to showcase its efficiency. While considering the accuracy measure, the proposed model has attained 96.7 % accurate result on the analysis based on 500 hidden neurons.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"42 ","pages":"Article 100571"},"PeriodicalIF":4.2,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tangible progress: Employing visual metaphors and physical interfaces in AI-based English language learning","authors":"Mei Wang ,&nbsp;Hai-Ning Liang ,&nbsp;Yu Liu ,&nbsp;Chengtao Ji ,&nbsp;Lingyun Yu","doi":"10.1016/j.bdr.2025.100570","DOIUrl":"10.1016/j.bdr.2025.100570","url":null,"abstract":"<div><div>In this study, we aim to explore an interactive system that integrates visual metaphors, AI-powered essay scoring techniques, and tangible feedback to enhance students' English language learning experience. Over the past decade, AI has made significant strides across various domains, including education. A prominent example of this is the integration of AI-driven language learning tools featuring Automated Essay Scoring (AES) systems. Traditionally, AES relied on predefined criteria and provided scores in simple text formats, which often lack depth and fail to engage students in understanding their progress or areas for improvement. To address these limitations and enhance learnability, we propose a system that harnesses AI-powered AES with a visualization approach. Our system includes three main components: an AI-driven scoring algorithm, a visualization interface translating scoring outcomes into visual metaphors, and tangible postcards for presenting scores. To evaluate the usage of our visualization system and tangible-formatted feedback in practice, we conducted domain expert interviews and a three-stage user study. The results indicate that the progressive visual feedback and tangible postcards increased practice frequency and significantly boosted study motivation. Tangible visual feedback showed positive effects on fostering progressive learning. Through this study, we recognized the potential of combining AI, visual metaphors, and tangible feedback in English education to encourage continuous and active learning.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"42 ","pages":"Article 100570"},"PeriodicalIF":4.2,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exogenous variable driven cotton prices prediction: comparison of statistical model with sequence based deep learning models","authors":"G.Y. Chandan ,&nbsp;Prity Kumari","doi":"10.1016/j.bdr.2025.100569","DOIUrl":"10.1016/j.bdr.2025.100569","url":null,"abstract":"<div><div>This study investigates price forecasting model for cotton in Gujarat, India, using daily modal prices and arrival data sourced from Agmarknet spanning April 2002 to April 2023. Given the volatile and nonlinear nature of agricultural prices, this research integrates exogenous variables through statistical and advanced deep learning models to enhance predictive accuracy. The models tested include the Autoregressive Integrated Moving Average with Exogenous variables (ARIMAX), Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) and Stacked LSTM. Results reveal that Stacked LSTM model outperforms traditional statistical and basic neural network models, achieving the lowest values in accuracy metrics like Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE). With 365 days ahead forecast horizon, Stacked LSTM model yielded an error of 9.30% during pre-sowing season (May-June 2023) and 13.75% in harvesting season (October-November 2023). This precision in capturing seasonal price fluctuations can be attributed to the integration of relevant exogenous variables, which enhance the model’s ability to account for external market influences affecting cotton prices in Gujarat.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"42 ","pages":"Article 100569"},"PeriodicalIF":4.2,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145418507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"STED: An encoder-decoder architecture for long-term spatio-temporal weather forecasting","authors":"Haoran Gong,&nbsp;Lei Lei,&nbsp;Shan Ma,&nbsp;Chunyu Qiu","doi":"10.1016/j.bdr.2025.100568","DOIUrl":"10.1016/j.bdr.2025.100568","url":null,"abstract":"<div><div>Meteorological data is closely related to everyone's daily life, and accurate weather forecasting is crucial for many socio-economic activities. However, as a typical spatio-temporal data type, the complex temporal nonlinearity and spatial dependencies in meteorological data greatly increase the difficulty of forecasting. This paper proposes a neural network model, STED (Spatio-Temporal Data Encoder-Decoder), based on an encoder-decoder architecture, which effectively handles the temporal dynamics of long time series and high-precision spatial dependencies. STED consists of three modules: a spatial encoder-decoder, a temporal encoder-decoder, and a predictor. The spatial encoder-decoder extracts spatial features, the temporal encoder-decoder extracts temporal features, and the predictor is used for forecasting. Experimental results show that STED performs similarly to current state-of-the-art (SoTA) spatio-temporal forecasting models in short-term temperature prediction tasks, but significantly outperforms other models in medium- and long-term temperature prediction tasks. Additionally, this paper compares different spatial encoder-decoders for forecasting tasks with varying node scales. The experimental results demonstrate that, for small-scale node tasks, the spatial encoder-decoder based on multilayer perceptrons achieves good accuracy and efficiency. In contrast, for large-scale node tasks, the spatial encoder-decoder based on convolutional neural networks exhibits superior performance.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"42 ","pages":"Article 100568"},"PeriodicalIF":4.2,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145364825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive spectral GNN and frequency enhanced self-attention for traffic forecasting","authors":"Yongpeng Yang ,&nbsp;Zhenzhen Yang","doi":"10.1016/j.bdr.2025.100567","DOIUrl":"10.1016/j.bdr.2025.100567","url":null,"abstract":"<div><div>In intelligent city, traffic forecasting has played a significant role in intelligent transportation system. Nowadays, many methods, which combine spectral graph neural network and self-attention, are proposed. However, they still have some limitations for traffic forecasting: 1) The polynomial basis of traditional spectral graph neural networks (GNN) is fixed, which limits their ability to learn spatial dependency of traffic data. 2) Some GNNs ignore the dynamic dependency of traffic data. 3) Traditional self-attention suffers from limited perception for long-term information, time delay, and global information. These defaults pose big challenge for traffic forecasting via limiting their ability of capturing spatial-temporal dependency, dynamic and heterogeneous nature in traffic data. From this perspective, we propose an adaptive spectral GNN and frequency enhanced self-attention (ASGFES) for traffic forecasting, which can effectively capture the spatial-temporal dependency, dynamic and heterogeneous nature in traffic data. Specifically, we first introduce an adaptive spectral graph neural network (ASGNN) for effectively capturing the spatial dependency via conducting adaptive polynomial basis. In addition, two dynamic long and short range attentive graphs are fed into the ASGNN for emphasizing the dynamicity in view of long and short range. Secondly, we introduce a normalized self-attention with damped exponential moving average (NSADEMA). Specifically, the normalized self-attention (NSA) can capture the necessary expressivity to learn all-pair interactions without the need for some extra operation such as positional encodings, multi-head operations, and so on. It can well obtain the temporal dependency and heterogeneity of traffic data. In addition, the DEMA, which is equipped into NSA, can enhance the perception for the inductive bias of traffic data in time domain. It can be aware of the time delay of traffic data. Thirdly, linear frequency learner with time-series decomposition (LFLTD) are developed for enhancing the ability of capturing the temporal dependency and heterogeneity. Specifically, time-series decomposition (TSD) facilitates the analysis and forecasting of complex time via capturing various hidden components such as the trend and seasonal components. Meanwhile, linear frequency learner (LFL) can learn global dependencies and concentrating on important part of frequency components with compact signal energy. At last, many experiments are performed on several public traffic datasets and demonstrate the proposed ASGFES can achieve better performance than other traffic forecasting methods.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"42 ","pages":"Article 100567"},"PeriodicalIF":4.2,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145271154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A decentralized metaheuristic approach to feature selection inspired by social interactions within a societal framework, for handling datasets of diverse sizes","authors":"Sobia Tariq Javed ,&nbsp;Kashif Zafar ,&nbsp;Irfan Younas","doi":"10.1016/j.bdr.2025.100556","DOIUrl":"10.1016/j.bdr.2025.100556","url":null,"abstract":"<div><div>The rapid advancement of technology has led to the generation of big data. This vast and diverse data can uncover valuable patterns and yield promising results when effectively mined, processed, and analyzed. However, it also introduces the “curse of dimensionality,” which can negatively impact the performance of machine learning models. Feature Selection (FS) is a data preprocessing technique aimed at identifying the optimal feature set to enhance model efficiency and reduce processing time. Numerous metaheuristic wrapper-based FS techniques have been explored in the literature. However, a significant drawback of many of these algorithms is their dependence on centralized learning, where the global best solution drives the search direction. This centralized approach is risky, as any error by the global best can hinder the exploration and exploitation of other potential areas, leading to inaccuracies in discovering the true global optimum. In this paper, the binary variant of a novel decentralized metaheuristic Kids Learning Optimization Algorithm (KLO) called <strong>Binary Kids Learning Optimization Algorithm (BKLO)</strong> is proposed for optimal feature selection for classification purposes in wrapper mode. The continuous solutions of KLO are converted to binary space by using the transfer function. A comparison is provided between the two transfer functions: hyperbolic tan (V-shaped) and the Sigmoidal (S-shaped) transfer functions. BKLO is compared with seven state-of-the-art algorithms. The performance of algorithms is evaluated and compared using several assessment indicators over fifteen benchmark datasets with a wide range of dimensions (small, medium, and large) from the University of California Irvine (UCI) repository and Arizona State University. The superiority of BKLO in reducing the number of features with increased classification accuracy over the other competing algorithms is demonstrated through the experiments and Friedman's Mean Rank (FMR) statistical tests.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"41 ","pages":"Article 100556"},"PeriodicalIF":4.2,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Compression of big data collected in wind farm based on tensor train decomposition","authors":"Keren Li ,&nbsp;Wenqiang Zhang ,&nbsp;Dandan Xiao ,&nbsp;Peng Hou ,&nbsp;Shuai Yan ,&nbsp;Yang Wang ,&nbsp;Xuerui Mao","doi":"10.1016/j.bdr.2025.100554","DOIUrl":"10.1016/j.bdr.2025.100554","url":null,"abstract":"<div><div>To address the storage challenges stemming from large volumes of heterogeneous data in wind farms, we propose a data compression technique based on tensor train decomposition (TTD). Initially, we establish a tensor-based processing model to standardize the heterogeneous data originating from wind farms, which includes both structured SCADA (supervisory control and data acquisition) data and unstructured video and picture data. Subsequently, we introduce a TTD-based method designed to compress the heterogeneous data generated in wind farms while preserving the inherent spatial eigenstructure of the data. Finally, we validate the efficacy of the proposed method in alleviating data storage challenges by utilizing authentic wind farm datasets. Comparative analysis reveals that the TTD-based method outperforms previously proposed compression techniques, specifically the canonical polyadic (CP) and Tucker methods.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"41 ","pages":"Article 100554"},"PeriodicalIF":4.2,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable malware detection through integrated graph reduction and learning techniques","authors":"Hesamodin Mohammadian,&nbsp;Griffin Higgins,&nbsp;Samuel Ansong,&nbsp;Roozbeh Razavi-Far,&nbsp;Ali A. Ghorbani","doi":"10.1016/j.bdr.2025.100555","DOIUrl":"10.1016/j.bdr.2025.100555","url":null,"abstract":"<div><div>Recently, Control Flow Graphs and Function Call Graphs have gain attention in malware detection task due to their ability in representation the complex structural and functional behavior of programs. To better utilize these representations in malware detection and improve the detection performance, they have been paired with Graph Neural Networks (GNNs). However, the sheer size and complexity of these graph representation poses a significant challenge for researchers. At the same time, a simple binary classification provided by the GNN models is insufficient for malware analysts. To address these challenges, this paper integrates novel graph reduction techniques and GNN explainability in to a malware detection framework to enhance both efficiency and interpretability. Through our extensive evolution, we demonstrate that the proposed graph reduction technique significantly reduces the size and complexity of the input graphs, while maintaining the detection performance. Furthermore, the extracted important subgraphs using the GNNExplainer, provide better insights about the model's decision and help security experts with their further analysis.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"41 ","pages":"Article 100555"},"PeriodicalIF":4.2,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}