Big Data Research最新文献

{"title":"Cost optimization model design of fresh food cold chain system in the context of big data","authors":"Lei Wang ,&nbsp;Guangjun Liu ,&nbsp;Ibrar Ahmad","doi":"10.1016/j.bdr.2023.100417","DOIUrl":"10.1016/j.bdr.2023.100417","url":null,"abstract":"<div><p>The assessment of cold chain logistics for fresh products can be more precise with high-dimensional information data, providing valuable insights for the optimization of associated costs. Nonetheless, traditional data processing techniques fail to meet the processing efficiency required for such high-dimensional cold chain logistics data. Therefore, this paper proposes a spectral clustering algorithm based on the local standard deviation and optimized initial center, which comprehensively analyzes the fixed, transportation, refrigeration, and cargo damage costs of cold chain logistics. Additionally, this algorithm includes a variation operator based on clustering and introduces a large neighborhood search mechanism for optimizing the individual connectivity gene layer after selecting the gene layer site for variation. Simulation results demonstrate that the proposed algorithm exhibits better convergence in 15 iterations, reduces error rates, and significantly cuts down on the clustering process time. This ultimately leads to a reduction in the total cost of cold chain calculation.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"35 ","pages":"Article 100417"},"PeriodicalIF":3.3,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214579623000503/pdfft?md5=0db9cf3ef6ea7d1e1fd34d6a3e87e1ee&pid=1-s2.0-S2214579623000503-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135670379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 methodology to assess and evaluate sites with high potential for stormwater harvesting in Dehradun, India","authors":"Shray Pathak ,&nbsp;Shreya Sharma ,&nbsp;Abhishek Banerjee ,&nbsp;Sanjeev Kumar","doi":"10.1016/j.bdr.2023.100415","DOIUrl":"10.1016/j.bdr.2023.100415","url":null,"abstract":"<div><p>The urgency to protect natural water resources in a sustainable manner has risen as water scarcity and global climate change continue to worsen. Among various methods of collecting water, stormwater harvesting (SWH) is regarded as the most environmentally friendly approach to alleviating the strain on freshwater resources. The study introduces a robust approach to evaluating the potential for SWH, considering both technical and socioeconomic aspects. This method effectively identifies and assesses suitable areas, referred to as hotspots, for implementing SWH. Multiple criteria are established to quickly evaluate and analyze the suitability of these sites for stormwater harvesting. Moreover, the input from water experts is incorporated into the decision-making process. Initially, potential locations are chosen, and hotspots are identified based on the concept of accumulated catchments. Subsequently, a more detailed analysis is carried out on the shortlisted sites, utilizing multiple screening criteria such as demand, inverse weighted distance, and the runoff-to-demand ratio. A standardized method is then employed to rank the sites and determine the most suitable one for stormwater harvesting. The study identifies eight locations that are appropriate for SWH, with two of them being particularly suitable locations. Further, the radius of influence is added to encompass these sites in order to pinpoint the areas conducive to fulfilling water requirements and availability. This approach empowers water planners to make well-informed decisions in a more streamlined manner. Consequently, the methodology emphasizes the benefits of these tools for water experts who are actively seeking sustainable solutions to mitigate the pressure on freshwater resources.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"35 ","pages":"Article 100415"},"PeriodicalIF":3.3,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214579623000485/pdfft?md5=1736971c2f1584138324cb67603cb69a&pid=1-s2.0-S2214579623000485-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135614493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Wetland identification through remote sensing: Insights into wetness, greenness, turbidity, temperature, and changing landscapes","authors":"Rana Waqar Aslam ,&nbsp;Hong Shu ,&nbsp;Kanwal Javid ,&nbsp;Shazia Pervaiz ,&nbsp;Farhan Mustafa ,&nbsp;Danish Raza ,&nbsp;Bilal Ahmed ,&nbsp;Abdul Quddoos ,&nbsp;Saad Al-Ahmadi ,&nbsp;Wesam Atef Hatamleh","doi":"10.1016/j.bdr.2023.100416","DOIUrl":"10.1016/j.bdr.2023.100416","url":null,"abstract":"<div><p>Wetlands are important in many ways, including hydrological cycles, ecosystem diversity, climate change, and economic activity. Despite the Ramsar Convention's awareness programmes, the importance of wetlands is frequently disregarded in underdeveloped countries. The Ramsar Convention recognises 2491 wetlands worldwide, 19 of which are in Pakistan. The goal of this study is to use satellite sensor technology to identify neglected wetlands in Pakistan. The key goals of this research are to analyse water quality, monitor ecological changes, and comprehend the impact of climate change on the aforementioned wetlands. We used approaches like supervised classification and TCW to identify wetlands. To detect climate-induced changes, a change detection index was used to Quick Bird imagery. TCG and the NDTI were also employed to examine the water quality and ecological changes in these wetlands. Sentinel-2 data between 2016 and 2019 were used in the analysis. Furthermore, watershed analysis was carried out using ASTER DEM data. Modis data was used to calculate the LST (°C) of the selected wetlands, while rainfall (mm) data was collected from ANN databases. According to the study's findings, in 2016, Borith, Phander, Upper Kachura, Satpara, and Rama Lake held 22.73%, 20.79%, 23.01%, 24.63%, and 23.03% water, respectively. In 2019, the water ratios for these lakes were 23.40%, 22.10%, 22.43%, 25.01%, and 24.56%. These findings emphasise the need of taking preventative actions to protect these wetlands in order to improve ecosystem dynamics in the future. As a result, it is critical that the relevant authorities implement the necessary conservation measures.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"35 ","pages":"Article 100416"},"PeriodicalIF":3.3,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214579623000497/pdfft?md5=6c2fd850b51a67adc45a9dc630b4afe6&pid=1-s2.0-S2214579623000497-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135565832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"ML-aVAT: A Novel 2-Stage Machine-Learning Approach for Automatic Clustering Tendency Assessment","authors":"Harshal Mittal,&nbsp;Jagarlamudi Sai Laxman,&nbsp;Dheeraj Kumar","doi":"10.1016/j.bdr.2023.100413","DOIUrl":"https://doi.org/10.1016/j.bdr.2023.100413","url":null,"abstract":"<div><p>Clustering tendency assessment, which aims to deduce if a dataset contains any cluster structure, and, if it does, how many clusters it has, is a critical problem in exploratory data analysis. The VAT family of algorithms provides a “visual” means to assess the clustering tendency for various datasets. The VAT algorithm operates by reordering the pairwise distance matrix of the input data. When viewed as a monochrome image, this reordered dissimilarity matrix is called a reordered dissimilarity image (RDI), showing possible data clusters by dark blocks along the diagonal. This process, however, requires human intervention to interpret an RDI. Moreover, for datasets having complex cluster structure or noise, dark blocks along the diagonal of the RDI are not easily distinguishable, making it difficult to count them accurately, and different individuals can report different numbers of dark blocks. Only a handful of approaches have been proposed in the literature to automatically (algorithmically) infer the cluster structure from a VAT-type RDI without requiring human input. However, these approaches do not perform well for several data types and have impractically high run-time. This paper proposes and develops ML-aVAT: a novel two-stage machine-learning-based approach for automatic clustering tendency assessment from VAT-type RDI. Besides estimating the number of clusters, ML-aVAT can also infer the clustering hierarchy, i.e., sub-clusters within each group, something none of the previously proposed algorithms could do. Numerical experiments performed on various synthetic and real-life labeled and unlabeled datasets prove the effectiveness of ML-aVAT in estimating clustering tendency and cluster hierarchy.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"34 ","pages":"Article 100413"},"PeriodicalIF":3.3,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92043108","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":"Early Pathogen Prediction in Crops Using Nano Biosensors and Neural Network-Based Feature Extraction and Classification","authors":"Mohammad Khalid Imam Rahmani ,&nbsp;Hayder M.A. Ghanimi ,&nbsp;Syeda Fizzah Jilani ,&nbsp;Muhammad Aslam ,&nbsp;Meshal Alharbi ,&nbsp;Roobaea Alroobaea ,&nbsp;Sudhakar Sengan","doi":"10.1016/j.bdr.2023.100412","DOIUrl":"https://doi.org/10.1016/j.bdr.2023.100412","url":null,"abstract":"<div><p>The most prevalent microbe-caused issues that reduce agricultural output globally are viral and bacterial infections. It is currently quite challenging to identify pathogens due to the current living situation. Biosensors have become the standard for monitoring microbial and viral macromolecules. Disease diagnosis is improved by following the nanoparticles released by infections. Since the sensors' data includes different learning patterns, Machine Learning<span> (ML) methods are used to analyze and interpret it. This research paper aimed to study whether Near-infrared (nIR) and Red, Green, and Blue (RGB) imaging might be used to define and detect Plant Disease (PD) using Convolutional Neural Network (CNN)-based Feature Extraction (FE) and Feature Classification (FC). A home-built Single-Walled Carbon NanoTube (SWCNTs) implemented with a Deoxyribonucleic Acid (DNA) aptamer that binds to a Hemi (HeApt + DNA + SWCNT) sensing device was used to analyze near-infrared (nIR) and RGB images of tea plant leaf samples. Three labels are extracted from the nIR + RGB using a Wasserstein Distance (WD)-based Feature Extraction Model (FEM), and then all those labels are loaded into the proposed CNN model to ensure precise classification. The proposed Wasserstein Distance-to-Convolutional Neural Network (WD2CNN) model was compared to different CNN architectures on the same dataset, achieving the highest accuracy of 98.72%. It is also the most computationally efficient, with the shortest average time per epoch. The model demonstrates high performance and efficiency in classifying biosensor images, which could aid in the early detection and prevention of Crop Diseases (CD).</span></p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"34 ","pages":"Article 100412"},"PeriodicalIF":3.3,"publicationDate":"2023-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49711619","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":"End-PolarT: Polar Representation for End-to-End Scene Text Detection","authors":"Yirui Wu ,&nbsp;Qiran Kong ,&nbsp;Cheng Qian ,&nbsp;Michele Nappi ,&nbsp;Shaohua Wan","doi":"10.1016/j.bdr.2023.100410","DOIUrl":"https://doi.org/10.1016/j.bdr.2023.100410","url":null,"abstract":"<div><p>Deep learning has achieved great success in text detection, where recent methods adopt inspirations from segmentation to detect scene texts. However, most segmentation based methods have high computation cost in pixel-level classification and post refinements. Moreover, they still faces challenges like arbitrary directions, curved texts, illumination and so on. Aim to improve detection accuracy and computation cost, we propose an end-to-end and single-stage method named as End-PolarT network by generating contour points in polar coordinates for text detection. End-PolarT not only regress locations of contour points instead of pixels to relieve high computation cost, but also fits with intrinsic characteristics of text instances by centers and contours to suppress mislabeling boundary pixels. To cope with polar representation, we further propose polar IoU and centerness as key parts of loss functions to generate effective paradigms for text detection. Compared with the existing methods, End-PolarT achieves superior results by testing on several public datasets, thus keeping balance between efficiency and effectiveness in complicated scenes.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"34 ","pages":"Article 100410"},"PeriodicalIF":3.3,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49733799","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":"Study on the Temporal and Spatial Evolution Characteristics of Chinese Public's Cognition and Attitude to “Double Reduction” Policy Based on Big Data","authors":"Jiahui Liu ,&nbsp;Wei Liu ,&nbsp;Chun Yan ,&nbsp;Xinhong Liu","doi":"10.1016/j.bdr.2023.100411","DOIUrl":"https://doi.org/10.1016/j.bdr.2023.100411","url":null,"abstract":"<div><p><span><span>The “double reduction” policy is a policy innovation of China's comprehensive education reform to build a high-quality education system. The public's cognition and attitude toward it are of great significance to its actual implementation. A total of 98396 texts related to “double reduction” collected from Sina-Weibo by web crawler technology are investigated to explore the public's cognition and attitude towards the “double reduction” policy as well as its spatio-temporal evolution characteristics. Guided by life cycle theory, the evolution of the public's attitude is studied by </span>sentiment analysis based on the ERINE algorithm and DUTIR. Topics are selected with the adoption of TF-IDF and </span>LDA models to perform spatio-temporal evolution of public cognition and analyze group differences. The results are as follows: the evolution of public concern about the “double reduction” policy is phased and the period of high incidence is closely related to time nodes such as policy release, the new school term, and holidays. There are temporal and spatial differences in the evolution of public attitudes between different stages and groups. Although the public holds a relatively negative attitude, with more information about the “double reduction” policy available, the public's attitude is gradually easing. Topics of public concern vary in different periods, and different groups show different emotional attitudes and have distinctive evolution characteristics of cognitive themes. Compared with other age groups, teenagers pay more attention to topics related to their studies and life. The government's official micro-blog not only shoulders the responsibility of publicizing relevant policies, but also pays close attention to the implementation of relevant policies around the country. The influential groups hold a relatively firm attitude and stable emotions and often can orient public opinions. The regional attention to the “double reduction” policy is positively correlated with the level of local economic development. The research results can help government departments learn about the public's cognition and attitude towards the “double reduction” policy to provide decision-making support, and serve as an important basis for solving existing contradictions and promoting the effective implementation of policies.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"34 ","pages":"Article 100411"},"PeriodicalIF":3.3,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49733798","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":"An Improved CycleGAN for Data Augmentation in Person Re-Identification","authors":"Zhenzhen Yang ,&nbsp;Jing Shao ,&nbsp;Yongpeng Yang","doi":"10.1016/j.bdr.2023.100409","DOIUrl":"https://doi.org/10.1016/j.bdr.2023.100409","url":null,"abstract":"<div><p>Person re-identification (ReID) has attracted more and more attention, which is to retrieve interested persons across multiple non-overlapping cameras. Matching the same person between different camera styles has always been an enormous challenge. In the existing work, cross-camera styles images generated by the cycle-consistent generative adversarial network<span> (CycleGAN) only transfer the camera resolution and ambient lighting. The generated images produce considerable redundancy and inappropriate pictures at the same time. Although the data is added to prevent over-fitting, it also makes significant noise, so the accuracy is not significantly improved. In this paper, an improved CycleGAN is proposed to generate images for achieving improved data augmentation. The transfer of pedestrian posture is added at the same time as transferring the image style. It not only increases the diversity of pedestrian posture but also reduces the domain gap caused by the style change between cameras. Besides, through the multi-pseudo regularized label (MpRL), the generated images are assigned virtual tags dynamically in training. Through many experimental evaluations, we have achieved a very high identification accuracy on Market-1501, DukeMTMC-reID, and CUHK03-NP datasets. On the three datasets, the quantitative results of mAP are 96.20%, 93.72%, and 86.65%, and the quantitative results of rank-1 are 98.27%, 95.37%, and 90.71%, respectively. The experimental results fully show the superiority of our proposed method.</span></p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"34 ","pages":"Article 100409"},"PeriodicalIF":3.3,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49711263","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":"Classifier-Based Nonuniform Time Slicing Method for Local Community Evolution Analysis","authors":"Xiangyu Luo ,&nbsp;Tian Wang ,&nbsp;Gang Xin ,&nbsp;Yan Lu ,&nbsp;Ke Yan ,&nbsp;Ying Liu","doi":"10.1016/j.bdr.2023.100408","DOIUrl":"https://doi.org/10.1016/j.bdr.2023.100408","url":null,"abstract":"<div><p>With the rapid expansion of the scale of a dynamic network, local community evolution analysis attracts much attention because of its efficiency and accuracy. It concentrates on a particularly interested community rather than considering all communities together. A fundamental problem is how to divide time into slices so that a dynamic network is represented as a sequence of snapshots which accurately capture the evolutionary events of the interested community. Existing time slicing methods lead to inaccurate evolution analysis results. The reason is that they usually rely on a linear strategy while the community evolution is a nonlinear process. This paper investigates the problem and proposes a classifier-based time slicing method for local community evolution analysis. First, a classifier is trained for judging whether there is a community in the given network snapshot is identified as the continuing of the community defined by the given node subset. The features for classification include internal cohesion degree and external coupling degree. Second, a time slicing method is proposed based on the trained classifier. As the network evolves, the method continuously uses the classifier to predict whether there is a community in the newest network identified as the continuing of the interested community. Whenever the answer is negative, an evolutionary event is presumed to have occurred and a new time slice is generated. Experimental results show that compared with existing time slicing methods, our proposed method achieves higher recognition rate for given redundancy ratio.</p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"34 ","pages":"Article 100408"},"PeriodicalIF":3.3,"publicationDate":"2023-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49733790","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 Multi-View Filter for Relation-Free Knowledge Graph Completion","authors":"Juan Li ,&nbsp;Wen Zhang ,&nbsp;Hongtao Yu","doi":"10.1016/j.bdr.2023.100397","DOIUrl":"https://doi.org/10.1016/j.bdr.2023.100397","url":null,"abstract":"<div><p>As knowledge graphs are often incomplete, knowledge graph completion methods have been widely proposed to infer missing facts by predicting the missing element of a triple given the other two elements. However, the assumption that the two elements have to be correlated is strong. Thus in this paper, we investigate <em>relation-free knowledge graph completion</em> to predict relation-tail(r-t) pairs given a head entity. Considering the large scale of candidate relation-tail pairs, previous work proposed to filter r-t pairs before ranking them relying on entity types, which fails when entity types are missing or insufficient. To tackle the limitation, we propose a relation-free knowledge graph completion method that can cope with knowledge graphs without additional ontological information, such as entity types. Specifically, we propose a multi-view filter, including two intra-view modules and an inter-view module, to filter r-t pairs. For the intra-view modules, we construct <em>head-relation</em> and <em>tail-relation</em><span> graphs based on triples. Two graph neural networks are respectively trained on these two graphs to capture the correlations between the head entities and the relations, as well as the tail entities and the relations. The inter-view module is learned to bridge the embeddings of entities that appeared in the two graphs. In terms of ranking, existing knowledge graph embedding models are applied to score and rank the filtered candidate r-t pairs. Experimental results show the efficiency of our method in preserving higher-quality candidate r-t pairs for knowledge graphs and resulting in better relation-free knowledge graph completion.</span></p></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"33 ","pages":"Article 100397"},"PeriodicalIF":3.3,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49711261","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}